Areas of predictive maintenance
Predictive maintenance involves condition monitoring and using ML to predict equipment failures before it happens. Avoid the digital leap of faith by choosing the best option for your business.
Predictive maintenance involves condition monitoring via sensors and using machine learning to predict equipment failures before it actually happens. This can be done by collecting data about the state of products built by the machinery, waiting for any products to diverge from the rest, a machine learning algorithm highlighting the part of the machinery that is malfunctioning, and fixing that part of the machine before it becomes a problem.
There are many other types of maintenance, such as corrective maintenance, preventative maintenance, risk-based maintenance, condition-based maintenance, and predetermined maintenance. However, predictive maintenance techniques increase cost savings by decreasing operational costs and expensive unplanned downtime. Tools in predictive maintenance include vibration analysis, infrared analysis, the Internet of Things, and machine learning. This article will also discuss the various use cases of predictive maintenance, especially with the Internet of Things.
Only a holistic and balanced approach to cost optimization can help executives avoid the digital leap of faith by choosing the best option for their business.
Types of maintenance
Before getting deeper into predictive maintenance and predictive maintenance technology, it’s important to recognize the different approaches to maintenance that exist. Each situation will call for different types of maintenance, and there is no one-size-fits-all solution.
Correct maintenance, or reactive maintenance, involves identifying faults in equipment and fixing them to make the equipment work again, whether through replacing, repairing, or restoring it. Ideally, corrective maintenance is planned and goes along a maintenance plan. When unplanned, the fix is more expensive due to unbudgeted costs and unplanned resource usage, like calling in the maintenance team past normal hours. This approach is very different from predictive maintenance, which aims to predict and prevent failures rather than respond to them.
This type of maintenance is typically not preferred due to possibly leading to downtime, increased workload, and loss of production or time. However, for non-essential items that can fail without causing issues and are quick to repair, it may be ideal to run to failure. This includes space crafts, where maintenance is hard, and they can be abandoned after failure.
Preventive maintenance tries to prevent machine breakdowns from even occurring by looking into the machine’s history and trying to identify when the machine is likely to break down again. While it sounds similar to predictive maintenance, preventive maintenance is scheduled on a regular basis, while predictive maintenance work is based on when the machine is predicted to fail.
Risk-based maintenance is often used for risk-sensitive systems by finding the cheapest way to use resources to minimize or repair risk. In other words, it prioritizes resources toward the systems in which failure would have the biggest risk. Risk can be assessed in many ways, and there are both qualitative and quantitive approaches employed in the industry. For example, if the entire company relies on a single server to keep its entire website online, it carries a lot of risks and should be prioritized over a server that isn’t hosting anything significant. Conversely, predictive maintenance doesn’t consider risk as much.
Condition-based maintenance measures equipment’s condition over time with sensors, and maintenance is performed when the sensors detect that performance has decreased. This condition monitoring is highly reliable in preventing cost-heavy failures from occurring, although it can be complex to set up and maintain. Compared to predictive maintenance, condition-based maintenance is performed when sensor data reaches an unacceptable level rather than when a machine is predicted to fail.
Predetermined maintenance uses manufacturer-provided programs based on the manufacturer’s knowledge of failure times. This is one of the least popular types because it is based on the manufacturer’s assumptions, and many organizations opt for other types of maintenance.
Tools in Predictive Maintenance
Predictive maintenance (PdM) has revolutionized maintenance practices by enabling organizations to proactively monitor equipment conditions and prevent failures. To achieve optimal results, selecting the right PdM tools is crucial. In this section, we will explore various PdM tools, including vibration analysis, ultrasonic analysis, internet of things and machine learning techniques.
Vibration Analysis for Predictive Maintenance
Vibration analysis involves using sensors to detect vibrations emitted by assets. By analyzing vibration readings, maintenance teams can identify known problem signals and detect changes over time, providing valuable information for action. Vibration analysis measures parameters such as velocity, displacement, and frequency to determine the type of vibration detected. It enables real-time data gathering, detection of potential problems, identification of failing mechanical components, equipment alignment assessment, and verification of proper installation and service.
Ultrasonic Analysis for Predictive Maintenance
Ultrasonic analysis utilizes sensitive microphones to capture high-frequency sounds emitted by assets. These sounds are converted into audio and digital data, which are then analyzed by humans or computer software. By comparing the collected data with known potential issues or previous recordings, ultrasonic analysis provides insights into asset performance and condition. It can be used for leak detection, failed steam trap detection, electrical inspection, valve testing, optimal lubrication practices, and more. Portable ultrasonic sensors enable data collection for immediate use or uploading to a database for further analysis.
Infrared Analysis for Predictive Maintenance
Infrared analysis utilizes infrared radiation (IR) to determine temperature differences between components in an asset. By comparing IR images or temperature readings over time, maintenance teams can assess asset condition and performance. Infrared analysis is valuable for detecting temperature variations of mechanical components, assessing the condition of electrical components (useful for ARC flash analysis), monitoring process temperatures, evaluating insulation or building conditions, assessing piping and plumbing conditions, and even analyzing solar panel conditions. It helps uncover potential problems that are not visible to the naked eye and can be conducted from a safe distance.
Internet of Things (IoT) for Predictive Maintenance
The Internet of Things (IoT) is at the forefront of transforming predictive maintenance. IoT enables the seamless collection of real-time data from connected assets and systems. Through IoT sensors and devices, organizations can monitor and capture crucial data on various parameters such as temperature, pressure, and vibration.
This continuous monitoring facilitates the detection of anomalies and potential failures, empowering maintenance teams to take proactive measures before catastrophic breakdowns occur. By leveraging IoT, organizations can implement predictive maintenance strategies that optimize equipment performance, reduce maintenance costs, and enhance operational efficiency.
Machine Learning (ML) in Predictive Maintenance
Machine Learning (ML) algorithms have become instrumental in analyzing vast amounts of data collected by IoT devices. By applying ML techniques, organizations can develop sophisticated predictive models capable of identifying patterns, trends, and correlations within the data. ML algorithms excel at predicting equipment failures, estimating remaining useful life (RUL), and optimizing maintenance schedules based on historical data and real-time inputs.
This capability to forecast failures in advance empowers maintenance teams to take timely intervention, thus minimizing downtime and reducing maintenance costs. The integration of ML in predictive maintenance facilitates intelligent decision-making, optimizing asset management strategies to ensure peak operational performance.
Predictive maintenance use cases
Implementing predictive maintenance is useful in many different settings and cases, especially using the Internet of Things. It can be used to monitor the quality of work done by machinery, monitor hydraulic valve lubricants and filters, monitor the tightening process of screws and nuts, monitor cooling systems, monitor the quality of painting processes, and analyze the vibrations of milling machines.
Monitoring Work by Machinery
One practical way that predictive maintenance can be used is to monitor the work quality of machinery and equipment. For example, data collected by sensors can be accumulated throughout the Internet of Things running the machinery; this data can uncover any inconsistencies in production by the machines, therefore revealing potential system failures and equipment malfunctions.
After the production of hydraulic valves, the cleanliness of the oil used to test it is expected to meet the international standard of cleanliness in order to ensure that the valves work properly. This is where the Internet of Things comes in. Sensors can be placed on test branches in order to collect actionable data about the cleanliness of the oil, and therefore the state of the hydraulic valve system.
If it reaches a certain level of pollution, data would be sent via the sensors through the Internet of Things to a mobile or web-based front-end platform, where employees can see clearly what needs to be fixed. Here, the Internet of Things conducts oil analysis and performs predictive maintenance as it communicates the information to whomever it concerns to fix it before it becomes an issue.
Monitoring Screw / Nut Tightening Processes
Many products need to be put together in factories before they are sent out to consumers, and they are held together by screws, nuts, and bolts. In order to maintain consistency among all copies of a product, companies must establish a certain degree to which each nut and bolt is screwed in. This way, no copy of a product is more weakly put together than another. This process can use a predictive maintenance program and the Internet of Things, as nut runners can be connected across different locations of equipment, and the degree to which each nut and bolt is screwed can be closely monitored and communicated across these runners through the Internet of Things.
Monitoring Cooling Systems
The Internet of Things can also be used in predictive maintenance programs to monitor cooling systems and ensure that there are no blockages causing pipe damage. Temperature and flow sensors throughout the pipe system can communicate through the Internet of Things in order to monitor any potential equipment failure.
Quality Assurance of Painting Processes
In any context, it is important to remain consistent when painting shapes and images. For this to happen, one must monitor every aspect of the process, such as the quality and quantity of the paint, and the shade of the color used. All these aspects can be monitored via sensors, and communicated through the Internet of Things as actionable data that indicates potential equipment failure.
Analyzing Vibrations of Milling Machines
The final predictive maintenance use case involves milling machines. Milling machines are tools that cut and drill through materials to create shapes. It does this by vibrating and rotating the cutter; different vibrations indicate different settings the milling machine could operate under. Data about the vibrations at each setting can be averaged, so if it ever vibrates at a speed far from the average vibration speed analyzed at that specific setting, then actionable information about it can be communicated through the Internet of Things to a front-end device, to someone who can fix the equipment malfunction.
Among the various types of maintenance, implementing a predictive maintenance solution continues to be the most cost-effective and efficient. There are many tools that can be implemented in predictive maintenance solutions, such as vibration analysis, infrared analysis, the Internet of Things, and machine learning.
These tools and predictive maintenance technologies, especially the Internet of Things, can be used in a wide variety of scenarios, including monitoring work by machinery, oil analysis, monitoring screw/bolt tightening processes, monitoring cooling systems, monitoring the quality of painting processes, and vibration analysis of milling machines. Predictive maintenance plays a big role in many industries and will continue to do so for the foreseeable future.