In an era where technology reigns supreme, industries of all kinds are adapting to the possibilities offered by artificial intelligence (AI). One field that has undergone a significant transformation is recruitment. With the emergence of AI recruiting software, the traditional methods of finding the right candidates have evolved into a more efficient and effective process. In this article, we delve into the world of AI recruiting software and its role in shaping the future of recruitment, both for organizations and recruitment agencies.

The Rise of AI Recruiting Software: Unveiling Innovation

AI recruiting software is not just a buzzword; it’s a game-changer. This technology marries the capabilities of AI and machine learning with the intricacies of talent acquisition, creating a synergy that revolutionizes the recruitment landscape.

Understanding AI Recruiting Software

Automated Sourcing: AI recruiting software scans a plethora of sources, including job boards and social media platforms, to identify potential candidates who match specific criteria. It dramatically reduces the time and effort needed for initial candidate searches.

Data-Driven Decision Making: These tools analyze candidate profiles and their qualifications in relation to job requirements, helping recruiters make informed decisions based on data rather than intuition.

Streamlined Screening: AI recruiting software can assess resumes and applications, shortlisting candidates who best fit the role. This not only saves time but also ensures that no hidden gems are overlooked.

Elevating Recruitment Agencies with AI Recruiting Software

Recruitment agencies are no strangers to the competitive nature of talent acquisition. The integration of AI recruiting software brings a range of benefits that can give these agencies an edge.

Enhancing Candidate Matching

Precision and Speed: AI recruiting software swiftly sifts through a vast pool of candidates to identify the ones who match the job requirements. This enables recruitment agencies to present their clients with suitable candidates faster than ever before.

Reducing Bias: Traditional recruiting methods can inadvertently introduce bias. AI recruiting software relies on data and qualifications, minimizing subjective judgments and promoting diversity and inclusivity.

Client Relationships: By delivering high-quality candidates in a shorter timeframe, recruitment agencies can strengthen their relationships with clients and become trusted partners in their hiring endeavors.

Synergy of AI Recruiting and Recruitment Agency Software

The power of AI recruiting software truly comes to light when combined with recruitment agency software. This combination creates a comprehensive ecosystem that empowers recruiters and enhances their ability to deliver exceptional results.

Streamlining Processes

Efficient Workflows: AI recruiting software automates many manual tasks, allowing recruiters to focus on building relationships and providing value to both candidates and clients.

Data Management: Integration with recruitment agency software ensures that candidate data seamlessly flows between systems, minimizing data entry efforts and reducing the chance of errors.

360-Degree View: Recruiters can access a holistic view of the candidate journey, from initial outreach to placement, within a single system. This facilitates better communication and collaboration.

Embracing the Future of Recruitment

As technology continues to advance, the role of AI in recruitment will only become more prominent. Organizations and recruitment agencies alike need to embrace this evolution to stay competitive and meet the demands of a rapidly changing job market.

Final Thoughts

AI recruiting software is the vanguard of recruitment innovation, reshaping the way organizations and recruitment agencies approach talent acquisition. The fusion of AI’s analytical prowess with the insights of recruitment agency software results in a dynamic synergy that elevates candidate matching, streamlines processes, and fosters stronger client relationships. In a world where finding the right talent is paramount, AI recruiting software emerges as a powerful ally that leads the way into the future of recruitment.

Author’s Bio:

Recruit CRM is on a mission to help recruiters across the world streamline their recruiting process using our intuitive and easy-to-use cloud-based ATS + CRM software. Check out our latest ebook “101 recruiting power boosters to ace your hiring in 2023” for expert recruiting tips and advice.

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Cloud Video Surveillance

Among the main advantages from a technological point of view is centralized control, the fact of being able to update multiple workstations from a central location (without sending the operator on site), and remote control. However, much-improved functionality due to the covid, which has pushed digitization. But the cloud had a major impact on the business model, as it shifted the buying process from capital expenditures to operational expenses. This offers end users greater flexibility with respect to the purchase of technology, which is actually rented/borrowed according to the needs of the moment. This generates different revenue streams than in the past, being monthly but continuous fees: the vendor will therefore have a regular cash flow and will be able to upsell the products by offering different additional services. The same integrators will have to change their way of doing business and, above all, their mindset by entering into a management logic rather than a sales one. 

Artificial intelligence

But the real star of the investigation is obviously artificial intelligence. If the automatic video analysis made it possible to detect any anomalies (unattended bag, improperly parked car, crossing the line) and then alert the operator, the AI ​​solves the problem of false alarms, thus automating security processes on a large scale. But there are also some gray areas to evaluate before implementation. 

Where is the data located?

Let’s start with the cloud, where the greatest risk is data localization. How can we be sure that the cloud provider is providing us with a secure service that prevents user names and access rights from being shared externally? And if a third party checks my data, how can I mitigate contract change costs? What will prevent a provider from charging me thousands of dollars to switch to another provider and get back the data he has stored? Or, coming to a very current example, with the conflict in Ukraine, it could happen that a cloud provider suddenly cut off all the cameras produced in a certain part of the world: you would find yourself with systems that do not work out of control. All issues are to be taken into consideration.

New questions to ask

Artificial intelligence involves a total mindset shift for security system integrators. It’s not just a matter of understanding the hardware but also of understanding how the underlying software works. System integrators must ask at least five questions to their suppliers:

1. The first set of questions aims to verify the AI ​​performance as claimed by the manufacturer. Does the performance meet customer requirements? Is it easy to fool? What happens if you wear a disguise? Will the false alarms return?

2. The second set of questions is about privacy. How does AI fit into the privacy and data protection measures? The more advanced the AI, the more metadata is collected. Age, gender, what you wear, the car you drive, make, model and color are all collected and stored. Does all this expose you and your customers to any regulatory compliance risk?

3. Can you explain how AI works? Is this a magic box, or is it possible to explain why the algorithm made a specific decision?

4. The fourth set of questions concerns possible bias: Is there any bias in the way AI controls the outcome? For example, if the algorithm was only trained on Caucasians, what would it do when dealing with Asian facial features or people of color? Will the result be distorted? What is the quality of the dataset used to train the AI? 

5. Camera manufacturers offering AI rarely have an in-house team that develops algorithms. They typically use the training sets and algorithms available on the market. Therefore, when evaluating a new AI system, you need to ask what kind of dataset it uses, what its origin is, and whether it was lawfully obtained.

The Clearview AI case speaks volumes: can using such a solution get me in trouble? Where does the training set come from? Where does the algorithm come from? Could two vendors use the same training set and algorithm? And if so, how do they differ?

In the market of the future

In the coming years, software development capabilities will be the real focus: competition between vendors will be based on software capabilities rather than catalog size or price. Many AI companies will enter the market, and the competition between them will revolve around three main elements.

1. Metadata. Which vendor can give us better metadata extraction from video feeds or access control systems? How many attributes can be extracted from the image? What level of detail can be achieved?

2. Quality of inference. As you know, the same things can appear very different with varying light conditions. For example, a silver car can look white at night. Algorithms that can guarantee more accurate results and provide better inference from their engine will benefit.

3. Discover and create links between different attributes. One of the key uses of artificial intelligence today is in forensic research. Now we can type in the system: “I’m looking for a man with a blue shirt and black pants.” The next step will allow the system to automatically identify which car it arrived in, the vehicle’s make and model, and the license plate number. 

Artificial Intuition

Finally, we come to the Holy Grail of artificial intelligence solutions: artificial intuition. The human brain is able to make decisions even in totally new situations, thanks to experience and instinct. For algorithms, this is not possible. Not yet, at least, because as technology evolves and neural networks are refined, we will be able to see computerized systems with a certain intuition or the ability to understand new situations and therefore decide, independently, the best course of action. And this will naturally also open up an ethical issue.

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In fact, it has already been widely demonstrated how video and audio can be used as a gateway for malware, and the algorithms used for deep fakes are becoming increasingly advanced. The Artificial Intelligence system activated the “detonator” through identification through biometric parameters. Some systems managed to remain silent, activating only when the intended victim was reached.

However, AI itself can – and in many examples, it already does – support and make Cyber ​​Security processes more efficient.

One component is enough.

Let’s start from the beginning and from a fundamental assumption: everything becomes central and primary; the periphery and the residual no longer exist. Equipment and individual subsystems are connected to each other and, in turn to users, as part of a single large “organism” that can be “attacked” not only directly in its “critical infrastructures” but violating any of its components – even residual ones – which then acts as a “bridge” to enter the heart of the main objective. It is, therefore, necessary to know and understand the criticalities brought about by technological convergence with relative global connection, to use all the advantages, minimizing the risks that can be mitigated by adopting technological, architectural, and procedural measures that are consistent and proportionate to the context and to the asset to be protected are its material,

How to reduce the risk

First of all, it is necessary to determine the new vulnerabilities introduced and then to protect each element that is part of the system and the communication channels between them; in any case, the basic rules for measuring risk remain unchanged and use the same reference parameters to define the probability and extent of what can happen. It is, therefore, always important to clearly carry out a context analysis and define in detail which assets are to be protected and any offenders. All this also gives balance and sustainability to protection and prevention activities that are consistent with the real risks and consequences of a criminal act.

Watch out for “intelligent agents.”

New factors come into play, which we could call Intelligent Agents, that is, any entity capable of perceiving the environment around it through sensors and performing actions through actuators. The Internet Of Things, i.e., the network of equipment, sensors, and devices other than computers, connected to the Internet: any electronic device equipped with software that allows it to exchange data with other connected objects; Artificial Intelligence, the ability of a hardware system to solve problems or perform tasks and activities typical of the human mind and ability, which creates machines (hardware and software) capable of “acting” autonomously (solving problems, performing actions, making decisions, etc. .), and, last but not least, Machine Learning, a system able to learn independently and learn from its mistakes, based on algorithms that analyze data: by learning from them, it is able to make decisions and make predictions. 

Does artificial intelligence play on defense?

It must also be said that some organizations are turning to AI not so much to solve their future problems completely but rather to shore up their current defenses.

To better understand how to address cybersecurity challenges, a major survey of 850 senior executives from IT Information Security, Cybersecurity, and IT Operations across seven industries across ten countries was conducted, which revealed:

• that global Internet business traffic will triple by 2023;
• that the increase in cyber attacks on critical operations within a company requires advanced capabilities that can only be provided through the use of AI-based systems;
• the need for effective and timely protection systems is growing.

In response to continuous attacks, the new frontier of cybersecurity can only be AI because it is with AI that hackers launch their attacks. 

Nearly one in five organizations used AI before 2019, and adoption is set to skyrocket, with two out of three organizations planning to use it immediately; three out of four IT managers say using AI has enabled their organization to respond faster to breaches. 

Three out of five companies say that using AI improves the accuracy and efficiency of cyber analysts.

Processes and procedures

The process of identifying and organizing the procedures appears fundamental, which starts from the creation of the data platform and arrives at the definition of governance through fundamental factors such as collaboration with the outside world, the implementation of the SOAR chain (Security – Orchestration – Automation – Response) and the creation of a team of cyber analysts. 

Organizations need to build a roadmap that addresses and resolves issues related to infrastructure, data systems, application landscapes, skills gaps, best practices, governance, and the selection and implementation of use cases. Taking these actions will allow organizations to avoid unnecessary losses and, in some cases, add additional income sources.

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Let’s take a practical and very intuitive example. In supermarkets, a retail sector that has always remained open, even during the toughest lockdowns, cameras combined with sales analysis based on artificial intelligence can help managers to have a clear and real-time picture of the choices of the customer but also on age groups and time preferences, in order to be able to prepare truly “targeted” commercial strategies.

AioT

But truly, the IoT, the artificial intelligence applied to the Internet of Things, is entering an ever wider range of applications that need better data management to achieve maximum efficiency and optimization. Today, through these applications, it is possible, for example, to see where, even in a very large building, there is a water leak or a door that is not closed correctly. It is sufficient that the sensors are calibrated in order not only to detect the alarm but to transmit the data immediately so that it is displayed, perhaps even in 3D, by the management staff within the operating system. It is intuitive how powerful this solution is for data centers, airports, or industries.

Against Covid

Artificial intelligence applied to the IoT can also help companies and institutions to manage that social distancing which, amid the pandemic, has, in many cases, also become a legal obligation. Intelligent cameras with corporate AI can report attendance data on a single platform. Therefore, access to shopping centers, stations, or workplaces is only allowed when it can be done in real safety. Indeed, before the situation becomes critical, it is possible to set alert thresholds and also examine the flow histories to alert customers and prevent them from accessing the shop or supermarket. Particularly not secondary, intelligence makes it possible to make the data provided by the cameras completely anonymous, ensuring that people’s privacy is always protected.

Deep learning

Going well beyond this pandemic, which sooner or later, we all hope, will end, the intelligent analysis of deep learning, to stay with video surveillance, as regards object detection, offer security officers greater awareness of the situation and better possibility of verification, thus allowing immediate response to any threats. It is said, and it is true, that metadata lowers detection times from hours to minutes, if not seconds. 

Beyond Covid

Even specialized and tailor-made solutions for virus containment, such as thermal imaging cameras or mask detection, if they fully exploit artificial intelligence, will be able, in a very near future, to be used in other less “emergency” areas. . Sensors, cameras, network infrastructure, big data, cloud, all the components of artificial intelligence technology, once in the field, can easily be directed towards other functions: the whole heating unit, for example, for fire prevention or for access control of a person in a building. Also because the development of 5G, experts tell us, will allow the IoT to extend from smart home or commercial applications to those at an industrial level. 

Industrial IoT

Industrial IoT, in fact, makes processes efficient, productive, and innovative by enabling an architecture that provides real-time information on operating and business systems. The data is converted into instructions allowing machines to perform specific tasks. The AI-based system takes less time and can run continuously without errors. As a result, production efficiency improves and takes much less time. Of course, the key will always be the flexibility of the devices.  

An interesting market

In essence, it is a market in which it is worth investing. Recent research by Markets and Markets started from the very basics, the chips. The size of the global artificial intelligence market in this sector was $ 7.6 billion in 2020 and is expected to reach $ 57.8 billion by 2026, with a CAGR of 40.1%. The main drivers for the market are increasingly large and complex data, the improvement in computing power, and the growing adoption of deep learning and neural networks. These are those networks that, as the name implies, are modeled based on the functioning of our brain, certainly the most intelligent system that has ever been conceived. One cannot fail to focus on these data and technologies.

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AI now affects the daily lives of billions of people worldwide and will continue to do so in the future. Artificial intelligence is constantly evolving thanks to machine learning, or machine learning; intelligent systems continuously learn and evolve almost autonomously, although guided and controlled by human beings.

The world of work is also undergoing great changes, and in the future, we will see many existing professions changed forever. 

Every industry can benefit from AI, and its applications are nearly endless. This is why we want to talk to you, in short, about how artificial intelligence is applied in the modern world and why it should not be underestimated.

Let’s see some examples immediately:

Artificial Intelligence In Ordinary Life

AI is often subtle, you don’t notice it, but it is applied everywhere in our daily life. In particular, we always have a device with us that uses it: the smartphone.

Modern phones have installed a virtual assistant (the most common are Siri and Google Assistant), which use artificial intelligence to understand what we say and, consequently, offer us a solution to our requests.

But in addition to voice assistants, online advertisements also leverage AI to show ads relevant to your needs. Perhaps you have already noticed advertisements for a product after researching or even talking about it with someone.

Advertising is shown because the algorithm learns your current needs and shows you the most relevant ads. 

We have also seen other examples of applications of AI in daily life.

Artificial Intelligence In Entertainment

“What are we looking at today?”. How many times have you looked at the recommended streaming services like Netflix, Prime Video, and Disney +? 

They, too, use machine learning to provide you with products to consume that best suit your tastes. As you go through the catalog and watch movies and TV series, the algorithm learns what you like and suggests new titles to watch. 

Video games today also use AI; did you know that? For example, two major hardware manufacturers, NVIDIA and AMD, are developing a new system that leverages machine learning to show better graphics by reducing the required resources, thereby increasing video game performance and allowing you to create ever-larger game worlds and complex.

Artificial Intelligence In City Security

Airports, cities, stations, and other public places are lined with surveillance cameras controlled by people. But even in this area, the human being is helped by the machine to prevent potential crimes.

Some sophisticated video surveillance systems help the control guard prevent possible threats. The software is trained to control people’s movements and highlight possible suspicious cases to the operator. How? It’s always about machine learning.

The training takes place by “feeding” the program with real criminal episodes; in this way, after thousands of video and image analyses, it is able to understand the common behaviors implemented by criminals. 

Artificial Intelligence In Science

The advancement of human knowledge takes place today also thanks to this new mechanism of machine learning. A recent case is that of AlphaFold, exploiting this incredible technology to map and predict the structure of proteins in living beings.

This will allow the future to study them with the utmost precision and develop, for example, new drugs, make crops more resistant to parasites, and find new treatments for known diseases.

Astrophysics has also received many benefits from AI. A recent case is that of Kevin Schawinski and the research team of ETH Zurich. The team used artificial intelligence to map known galaxies to study their physical changes through predictions provided by reliable data.

The result? Schawinski and associates understood that galaxies become redder as they move from low-density to high-density environments, and their stars become less concentrated in the central part. 

Artificial Intelligence: Practical And Endless Applications

In short, artificial intelligence has infinite applications in every conceivable sector. The results are extraordinary today, but we are still in an immature phase. In the future, with the refinement of machine learning and the continued accumulation and study of data, our lives will change forever.

Also Read : The Explosion Of Artificial Intelligence In The World Of Work

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The theme of AI embraces multiple aspects of human life, from ethical, social, and human, to those of work, the transformation of employment as we know it today, and the social stability that these transformations put in crisis.

For a decade, it has seen an absolute explosion in the world of work, and its application has grown in many areas, from industry to crafts.

Less complex functions such as organizing, planning, controlling, managing information, and supporting functions are destined to be performed by machines in the future, while new skills are required of humans.

But suppose it is true that robots have eliminated and will eliminate many indispensable tasks until a few years ago. In that case, it is equally true that they have created as many, with fewer and less tiring and repetitive tasks.

And that’s not all.

Technology has exponential growth. This means that many of the jobs we will do in 10 years have not yet been invented.

In short, nothing new for technology historians. All technological revolutions have had more or less the same characteristics.

The difference lies precisely in the ever-increasing speed with which technological progress advances and the speed with which the world of work must adapt.

Regarding change, there are two lines of thought:

• The apocalyptic: they think that technology will take the place of man by eliminating all the jobs and activities connected to it
• The integrated: they think that artificial intelligence will increase productivity, profits, and wealth.

How Artificial Intelligence Is changing The World Of Work

In our daily experience, we are getting used to living with automation. Amazon GO supermarkets are now known without checkout counters or shop assistants.

The products taken from the shelves are monitored by sensors and cameras able to associate the customer with the products he puts in the cart and, once the shopping is finished, send the bill via the App.

Amazon also showed us how it is possible to manage warehouses in an automated way using robots equipped with artificial intelligence capable of optimizing spaces and controlling the movement of goods better than humans.

We could then talk about Tesla, the self-driving car that will save us the trouble of driving in traffic (which, guess what will drastically reduce in smart cities ) but which, at the same time, could put different crises. Professions, such as that of the truck driver, are among the most popular in the USA.

Technologies That Have Supplanted The Jobs Of The Past

There are many causes, and some are curious:

• The people who were in charge of lighting the street lamps
• Switchboard operators, a profession that has now taken on other connotations and that has been equipped with other tools
• The bowling pin straightener

Others, on the other hand, are being phased out, such as subway train drivers.

It almost sounds funny. Some professions seem almost ridiculous to think of them today, but once upon a time, they could not be done without.

Benefits Of AI in The World Of Work

The innovation brought by AI is mostly about highly repetitive jobs. Strong and weak artificial intelligence and deep learning promise to take the strain out of most repetitive physical jobs.

Other benefits are less well known—one of these concerns the issue of inclusion (which also means democratization of work). Artificial intelligence is creating work for even the most fragile categories, such as parents who need to work from home through smart working and the disabled, who can offer their contribution by making their intellectual skills available.

A Limitation Of AI In The World Of Work

The automation brought by AI has several advantages on the professional level, but inevitably they get disadvantages from the human point of view, mainly due to the isolation of workers.

In an intelligent work scenario, unless adequate measures are taken, the psychosocial well-being of the workers decreases.

The Future Of Artificial Intelligence In The World Of Work

The future is made up of organizations using artificial intelligence to improve efficiency and avoid repetitive and costly tasks.

On the other hand, companies will have to set up continuous learning programs capable of supporting their collaborators in the transition that will not be clear but progressive and, at least for now, continues with new technologies and ways of approaching them.

The Right Approach For The Workers Of The Future

The new organization of the world of work is leading to a redefinition of the role of people within it. 

Future workers will increasingly have to train to acquire the skills that allow them to collaborate efficiently with machines, even remotely. 

The skills will no longer be (and already are not) exclusively technical and professional, but above all, human. 

Trust, collaboration, responsibility, autonomy, and creativity will be just some of the skills required.

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Industry 4.0 and Digitization offer the ability to optimize operations and maintenance. Innovations such as digitization, artificial intelligence (AI), machine learning (ML), neural networks, and cloud computing have increased the ability to collect, analyze and monitor equipment health in real-time.

Thanks to this advanced monitoring equipment and analytical methods, engineers responsible for the reliable operation of the equipment can step up the pressure to implement predictive maintenance programs (PdM) to achieve profitability, including:

• optimization of the service life of key equipment;
• minimization of unplanned downtime;
• maintenance cost management;
• Improving the security and operability of the company.

Maintenance can be a profitable center

Energy and chemical/petrochemical production plants are very complex and complicated facilities. Many infrastructure facilities and items manage, contain, and store raw materials and process fluids and gases. Reducing downtime of process units and operations due to unusable equipment and systems is a central point for increasing a company’s profits.

Mechanical failure is the main cause of equipment accidents in the manufacturing industry, while equipment failures lead to 36% of unplanned downtime. Creating more sophisticated maintenance and planning programs increases the level of operational excellence and operability of the equipment.

“Smart” maintenance generates profit

According to a recent McKinsey report, PdM can increase device availability (both process units and devices) by 5 to 15%. Optimized PdM operations extend the life of key equipment by 20 to 40%. More importantly, PdM can effectively reduce maintenance costs from 18 to 25%. Improved monitoring and timely proactive maintenance significantly reduce repair and replacement costs for critical equipment and minimize unplanned downtime and lost productivity. In addition, an unexpected equipment failure can lead to losses greater than the acquisition value of the replaced equipment.

Advantages of PdM

The failure rate of the main equipment in the company

In general, rotary and piston devices have the highest number of failures. Vibration problems are the main causes of failures of rotating equipment, especially pumps. All rotating devices vibrate, but changes in vibration levels over time are indicative of possible problems. In the hydrocarbon processing industry, about 7% of the pumps are used to consume 60% of the money for pump maintenance and repairs. Finding and solving the root causes of vibration or temperature changes thus replaces mere treatment of symptoms. To prevent the recurrence of faults, pump operators must do their utmost to take routine maintenance procedures to the next level. The growing use of intelligent manufacturing strategies and cloud computing will help increase the integrity of PdM activities.

This is not a new concept

Since the 1970s, maintenance workers have begun to install piezoelectric load cells to monitor and detect pump and motor performance problems. Unfortunately, these initial methods encountered continuity problems in data collection. In question, the vibration sensors often operated at different frequencies and amplitudes and had their own initial symptoms.

Only experts could decipher the meaning of the data obtained from the sensors and convert them into usable information. Sensors were often lost or removed during routine maintenance. The primary sensors were physically connected to minicomputers or terminals via wires. Various contaminants, such as dirt and lubricating oils, have degraded the signals of these sensors. The technicians responsible for the reliable operation and maintenance of the equipment found that the initial vibration monitoring methods did not produce the desired results.

Advances in minicomputers, terminals, and portable sensors have contributed to the improvement of device monitoring programs. However, real-time information and data connectivity have remained limited due to computer hardware and software capabilities. Converting vibration sensor signals into usable information continued to be a time-consuming task. In addition, data trends and information were muted in databases and were not easily shared between users.

Remote monitoring equipment methods have been incorporated into preventive maintenance programs. The amount of data collected has never been a problem. In some cases, too much data has reduced the ability to find key information about the health of the facility. It remained a long-term problem to understand what the data obtained indicates the current state of the device. Simply put, you can’t effectively fix something you don’t understand.

Vibration monitoring methods constantly strive to provide and convert collected data into reliable real-time information. All too often, the implementation of regular and preventive maintenance programs reveals a deteriorating condition of the rotating equipment.

Real-time device monitoring

Over the last ten years, the use of wireless technologies, cloud computing, smart devices, and artificial intelligence (AI) has enabled device management through advanced PdM programs. While preventive maintenance is performed based on the manufacturer’s recommended plan, PdM takes maintenance activities to the next level. In PdM, real-time process and technology data generate trends and histories used to predict process equipment changes. Extending equipment uptime and shortening production processes through improved reliability/maintenance programs, such as advanced PdM technology, help increase the level of operational excellence and safety of the entire enterprise.

Advanced PdM analysis

For PdM programs to be effective, they require the presence of thorough and valid data, including analytical tools; this is the only way to decide on the basis of available information. Recent advances in AI and ML (machine learning) make it possible to analyze and convert large amounts of collected data into patterns. As part of rotary and piston device monitoring, advanced vibration sensors use cloud computing to record real-time data in a variety of formats. Innovative AI and ML algorithms, built on a combination of software and neural networks, convert and analyze wireless sensor data.

This information generates trends that identify normal and unhealthy operating activities. More importantly, AI algorithms “learn” from transmitted measured vibration data and distinguish between “normal” or unacceptable signals.

A performance relationship between key devices is possible using predictive analysis based on AI, ML, and neural networks. These validated analytical tools are used to identify the real causes of performance variations in rotary and piston equipment. Early fault identification allows optimal corrective action to be taken before significant equipment damage or failure occurs, minimizing repair costs, reducing unplanned downtime, and ensuring safe operation.

Visual or routine condition checks cannot observe deteriorating conditions of pumps and compressors. The AI ​​and ML algorithms identify patterns from history and detect performance degradation, as evidenced by variations in device trends. Equipment performance problems are identified much earlier than through traditional preventive maintenance methods. System-generated alarms alert maintenance technicians that further investigations are required.

AI-based predictive analysis also goes beyond mere failure reporting. Data and operating history are used for the failing equipment or component’s residual life estimate (RUL). Thanks to RUL, maintenance and reliability technicians have complete information available for repair planning and equipment replacement, which in turn has a minimal impact on the uptime of the entire process.

Data visualization

Unspecified data has only a limited value. The basis of PdM is data visualization. Advanced PdM uses AI-based analytical and neural networks to convert collected data into usable information. They usually include dashboards that allow users to see device status data and track trends quickly. With such graphics, technicians are able to easily interpret the health of the equipment/process unit and make more informed data-driven decisions. In addition, RUL estimates are summarized in a graphical form to provide centralized information.

Secure wireless technologies and mobile application connect advanced sensors to the cloud for analysis using AI and ML software. With full industrial Internet of Things (IIoT) and cloud computing applications, maintenance technicians can constantly monitor the health of process-critical devices. The ability to predict RUL and “time to failure” is invaluable. With such information, service and operations groups can schedule repairs in advance instead of just responding to an emergency shutdown or unplanned outage.

Case study: L&T Nabha power plant

L&T Nabha Power Plant is the first supercritical coal-fired power plant and one of the most efficient power plants in India. This facility operates two 700MW supercritical heat units and is a major electricity provider in the state of Punjab in northern India. As a major energy provider, the reliability of the Nabha power plant is a key issue for the region. The unplanned need for maintenance and shutdown of this power plant unit has a dramatic and adverse effect on the productivity and profitability of regional companies and residential customers. Unfortunately, there were three unplanned downtimes in this power plant in one year due to pump accidents.

This condensate pump experienced lingering problems with cavitation erosion and excessive vibration, which led to bearing failures and unplanned failure of the Nabha power plant unit.

In the field of electricity generation, pumps are a key technological device. The condensate pump is one of the pumps that play a crucial role in maintaining the steady operation of the equipment (Fig. 4). It is a horizontal vane pump with an output of up to 1,650 m³ / h with a discharge of 9 MPa (62 psi) at 986 rpm. Every day this pump is down, the plant loses up to $ 250,000 in lost revenue. As a result of unplanned maintenance or failure of this pump, the cost of repairs exceeds tens of thousands of dollars.

Condensate pumps very often experience unplanned downtime due to failures due to cavitation erosion. As the primary electricity provider in the region, the reliability of the condensate pumps was a top priority.

To prevent fault conditions and increase the operability of the entire unit, the plant’s responsible engineers decided to install real-time vibration monitoring and an advanced AI-based predictive analysis solution for all condensate pumps. The new monitoring strategy focused on the early detection of a pump and component failure. In addition to fault detection, this monitoring solution included AI-based algorithms that provide reliable RUL estimates before subsequent outages. Several advanced wireless monitoring sensors have been installed to address bearing failures with these condensate pumps:

• bearings on the driven side of the electric motor;
• bearings on the drive side of the electric motor;
• bearings on the drive side of the pump;
• bearings on the driven side at the pump.

Figure 5: Taking into account previous work orders for maintenance service work, the responsible technicians have chosen the optimal location for the state-of-the-art acoustic vibration sensors

The monitoring system used secure Wi-Fi sensors to collect and record measured vibration data via the cloud continuously. Cloud-based AI algorithms analyzed and processed the collected data.

Approximately six weeks after installing the advanced sensors and analytical system, the new monitoring program alerted maintenance personnel that the blade had failed. This resulted in cavitation erosion at the condensate pump. Maintenance personnel verified this fault with a portable vibration monitoring device and performed a partial disassembly to confirm damage to the pump blades visually. Damaged blades were temporarily repaired before the pump was put back into operation.

Thanks to the application of AI-based predictive capabilities as well as advanced vibration monitoring functions, the L&T Nabha power plant unit has avoided serious pump failure and unplanned downtime. Using an advanced AI-based PdM system, the remaining life before full failure was estimated at 25 days. This created sufficient time to schedule a pump replacement during an already scheduled maintenance intervention. The early intervention reduced the number of repairs needed and minimized interruptions in the operation of the entire unit.

It’s not just black boxes.

IIoT, cloud computing, and wireless technologies support AI-based data analysis as part of an advanced PdM program. With full use of AI and ML methods, engineers can detect anomalies or faults in key equipment well in advance of the failure mode. Advanced wireless vibration / acoustic sensors support PdM programs for real-time data collection and recording.

Figure 6: Advanced wireless acoustic sensors have been installed with a strong magnet and two-component epoxy to withstand the demanding operation of this pump

Using AI, ML, and neural network algorithms, advanced analytical tools develop historical trends of monitored devices or components. With a complete operational history, AI-based analytical tools identify changes in trend data and estimate the RUL of the monitored equipment. With RUL, maintenance personnel are able to take corrective action well before the failure itself and thus focus on keeping the equipment in safe operating mode. Advanced PdM programs support improved results-based maintenance schedules that extend equipment uptime and increase operational safety.

Also Read : Unlock The Potential Of Hybrid Artificial Intelligence

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Artificial intelligence (AI) platforms will lead to breakthrough business innovations, from manufacturing to consumers. In addition to the capabilities of today’s software, AI is a combination of several concepts that can align with each other and produce better results. It is essential to look at the widespread and complex problems that artificial intelligence can solve successfully.

AI tools have evolved to include knowledge systems, fuzzy logic, automated learning, neural networks, ambient intelligence, and genetic algorithms in the industrial sector. The performance and affordability of computing systems have expanded the capabilities of AI sensor applications.

Hybrid tools can also play a bigger role. Other AI technology advances that will impact sensor systems include data mining, multi-agent systems, and distributed self-organizing systems.

Hybrid AI systems

The tools and methods used by hybrid artificial intelligence have minimal computational complexity and can be implemented on small assembly lines, individual robots, or systems with simple microcontrollers. These approaches use ambient intelligence and combine different artificial intelligence tools to make the most of each technology; they include a more advanced framework compared to traditional artificial intelligence mechanisms.

Hybrid artificial intelligence systems that use elements of different artificial intelligence methods can have more strengths and fewer weaknesses. An example is a neuro-fuzzy system, which combines the indefinite processing of fuzzy systems with the power of artificial neural networks.

Hybrid AI can help with automation, manufacturing, and robotics, including welding programming. The current system consists of two software systems working in series, which create viable programs for the robot, thus helping to increase efficiency. The first system, the so-called CAD (Computer-Aided Design) model interpreter, accepts the CAD model and determines the required welds. This data is sent to the program generator, which reorients the required welds according to the actual physical orientation of the panel.

The program generator sequentially sends programs to the robot (usually one program per welding line). At this time, the communication method is the standard Transmission Control Protocol / Internet Protocol (TCP / IP), and all outgoing programs can be displayed as text files. Other software systems could be integrated into the existing system at the moment the robot programs are sent to the robotic system.

Industrial artificial intelligence tools include knowledge systems, fuzzy logic, automated learning, neural networks, ambient intelligence, and genetic algorithms. Image courtesy of L&T Technology Services

Combining sensor and logic systems

Artificial intelligence reduces costs and saves time in most applications. Machines read data from real objects and, by layering subsequent layers, create an object model from a series of cross-sections. Researchers are combining sensor systems and some powerful new technologies; the result is, for example, lower energy consumption, lower space requirements, and time savings, together with higher performance at lower costs.

AI can streamline communication, reduce errors, and extend sensor life.

Over the last decade, the industry has explored various opportunities to move toward the development and use of hybrid intelligent control systems across different operations that are capable of using multiple AI methods. Due to the relative lack of familiarity and technical barriers associated with using these tools, it may take another decade for technicians to recognize the benefits, but this area is of growing interest.

Hybrid artificial intelligence systems can bring long-term sustainable business benefits throughout the industry value chain. The company’s management must use best-in-class solutions to transform older systems into modern models.

Also Read : What Is “Experience-Based Robotics” That Realizes Intelligent Robots?

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Getting an accurate reading at home can sometimes be challenging. We’ve put together a few tips to help you get the most accurate reading possible when you’re taking your own blood pressure at home.

Avoid Measuring Under Stress/Duress

Stress is something that can cause your blood pressure to rise quickly. Whether you’re angry and irritated or perhaps something stressful has you a bit worked up, it’s best not to test when you’re feeling stressed or under duress as this probably won’t be an accurate reading.

In these cases, you’re sure to have a higher level than your typical numbers.
If you find that you can never find a good time that you aren’t worked up or stressed, it might be time to consider retiring. We hear Florida is quite nice! Ha

Take More Than Once a Day

It’s a good idea to take your blood pressure more than once a day to get accurate readings. This can help to determine if there is something in your day that perhaps triggers a change or whether it’s particularly high at a certain point in your daily routine.

We recommend using a morning approach for the first time. Don’t measure right after you wake up but be sure to measure before you take any medications. Then, you can use the evening time for your next readings. Don’t be afraid to read it 2-3 times at each of these timeframes for an accurate reading.

Quality Equipment

It is important to be sure that you have quality equipment for reading your blood pressure. If your equipment is not reliably accurate, then your readings won’t be either. Medical sites like sensoronics.com have medically-approved equipment for this purpose and many other things as well.

You can have your device checked for accuracy as well. Maybe take it to your doctor with you and check your equipment against their medical equipment to see if they are close and be sure you are using it properly.

Positioning

There are many small things that can add to your blood pressure. Even sitting wrong can make a difference. Here are some vital points for positioning that will help you get an accurate reading of your blood pressure as well.

• Do not take your blood pressure with a full bladder
• Try to avoid caffeine, alcohol, food, and tobacco within 30 minutes of taking a reading
• Don’t chat right before or during taking your blood pressure
• Use the same arm every time. Try to elevate that arm to a level that is similar in height to your heart at the time of the reading
• Always place a blood pressure cuff on your bare skin or only over light clothing layers

These are simple things you can do and each of them will make a difference in your reading to help you get a more accurate blood pressure reading at home.

Also read : How The Google Smart Speakers Compare To Each Other

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Have promoted a frame of predictive learning, which is a prediction based on data (experience) that integrates sensation and behavior and correction in real-time. He gave a lecture on the concept of “experience-based robotics” based on work and research cases with companies.

A System That Learns And Adapts In The Real World

The results obtained by deep learning have come to be seen in various forms. Many of them are capable of smoothly generating and recognizing images, videos, and sounds. The result leads to recognition of natural language and is being utilized in the dialogue between machines and humans.

However, most of the satisfactory performance is achieved by connecting a computer such as a PC or a cloud to a network. In fact, there are few cases where robots utilize deep learning in a stand-alone state. Still, it is quite difficult for robots to achieve the same intelligence as humans. “It’s not so easy to connect and transfer between the cyber and physical worlds. I’ve always been aware of where those gaps are,” Ogata said. He points out that “thinking about a system that learns and adapts in the real world, not just deep learning, may be an opportunity to think about the next AI (artificial intelligence).”

Currently, image recognition is becoming widely used as a function of robots that utilize deep learning. Image recognition technology is used for the degree of damage to objects and quality inspection. However, the difficulty in further evolving robot development is to incorporate the measurement results of sensors in the physical world into the model.

Therefore, a method of interpreting the sensor value and directly connecting it to the action without using a model was considered. A reward value is prepared for the difference between the robot’s sense (sensor value) and the action taken based on it. 

Reinforcement learning to learn higher strategies that give higher rewards by giving higher rewards to well-resulting sensory and behavioral combinations is currently the focus of attention in the robot industry. There is. However, it is quite difficult to create a reward function that is the basis for judging “good” or “bad”, and the number of learnings is tens of thousands to hundreds of thousands, which is quite large.

Since it is difficult to perform this enormous number of learnings on an actual machine, simulations will be used, but evaluation of the analytical model created from this simulation is also an issue. For Go and Shogi, optimization can be done by setting the reward function, but for robots that work in the real world, it is difficult. Basically, when trying to predict, it fails, so it is necessary to first learn the result of the failed action and then change the internal state that generates the action.

Also Read : 6 ways To Take Screenshots Of Your Windows 10 Computer

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