Using sensor devices, predictive maintenance (PdM) is a form of condition-based maintenance that monitors the state of assets. Real-time data from these sensor devices is utilized to forecast when maintenance is needed for the asset, preventing equipment failure. The most sophisticated kind of maintenance that is now offered is predictive maintenance.
Organizations using time-based building maintenance run the risk of carrying out insufficient or excessive maintenance. Reactive maintenance also involves performing maintenance at the expense of unplanned downtime, but only when necessary. These problems are resolved with predictive maintenance. With PdM, maintenance is only planned for an asset’s breakdown and only when certain requirements are satisfied.
Predictive maintenance in buildings were first used by organizations in the early years of the twenty-first century. Organizations employed a periodic or offline technique to monitor asset conditions. “The vibration measurements are taken periodically—one time per month in general—and the vibration is monitored by comparing previous measurements to new ones,” according to a report that documents three PdM case studies from 2001.
These days, asset condition is tracked continuously or online. Connecting an Internet of Things (IoT) sensor device to maintenance software also enables remote construction monitoring. A work order for an inspection is initiated when certain requirements are satisfied.
Table of Contents
What is Predictive Maintenance?
Using sophisticated downtime tracking software, predictive maintenance analyzes data to determine when maintenance should be done on your equipment. PdM uses sensors to continuously monitor the health and performance of your machine through software. When used properly, these sensors notify you when a failure is about to occur in the equipment, allowing you to arrange maintenance ahead of time and avoid any unplanned downtime.
For many businesses, PdM is the recommended maintenance management approach. Predictive maintenance reduces costs by requiring an initial investment, while reactive and preventative maintenance might grow expensive over time.
The Difference Between Predictive Maintenance and Preventive Maintenance
Predictive maintenance and preventive maintenance differ in a few ways, even though many maintenance plans combine elements of both. Regardless of whether the machinery needed maintenance or not, preventive maintenance has meant examining and maintaining it. Either a use or time trigger will determine the basis for this maintenance schedule. For instance, an automobile needs scheduled maintenance every 5,000 miles, while heating equipment needs to be maintained annually before winter.
Furthermore, unlike predictive maintenance, preventative maintenance does not require the condition monitoring component. A preventative maintenance program requires less capital investment in technology and training because it does not require condition monitoring. Finally, manual data collection and analysis are required for many preventative maintenance programs.
Predictive maintenance is recognized using various technologies and preset and predetermined conditions of individual pieces of equipment, whereas preventive maintenance is based on the typical life cycle of an asset. In the long term, predictive maintenance will save more money and time than preventive maintenance, but it also demands more investments in personnel, training, and equipment.
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Predictive Maintenance and Industry 4.0
The fourth wave of the industrial revolution, known as “Industry 4.0,” builds on and improves on the technological developments of the previous industrial revolution. With the advent of digital technology and automation, this earlier period—known as the third industrialization wave—brought forth new levels of efficiency. However, it also brought with it additional issues (such as data integrity, complexity in servicing and maintaining, higher costs, breaches, etc.). To revolutionize security and maintenance services, Industry 4.0 is tackling these new problems with advancements in data communication and technology.
New maintenance strategies that increase availability, lower costs, boost safety, and eventually eliminate unplanned downtime are made possible by the convergence of technological trends like the Internet of Things (IoT), cloud, big data and analytics, advanced analytics, machine learning, artificial intelligence, and augmented reality.
How does PdM Work?
Condition-based monitoring equipment and the Internet of Things (IoT) are critical components of a successful predictive maintenance program. An asset’s performance is evaluated in real time using condition-monitoring equipment, such as vibration analysis, acoustic monitoring, and infrared thermography. Through the embedding of sensors inside items, the Internet of Things facilitates the seamless transmission of data between these objects and systems.
IoT and condition monitoring devices work together to enable real-time data sharing and connectivity between sensors installed on equipment. This data “Talks” to each other, identifying patterns and establishing performance guidelines. Your team gets notified when a sensor identifies spikes or dips that are not within specified criteria, indicating that the equipment is close to failing.
However, by using historical data, you may also bring your assets closer to the brink of failure without really starting the failure, saving you from having to schedule unnecessary maintenance.
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1. Recognizing Failure Patterns
The most effective way for predictive maintenance to function is to identify an asset’s failure pattern using historical data, apply current data to that pattern, and then make predictions. This usually depends on machine learning, which allows programs to analyze enormous volumes of data in order to identify patterns. Based on the correlation between your real-time data and past patterns, the software may then make deft judgments.
You must be aware of the conditions to look out for in order for predictive maintenance to function. By utilizing machine learning on your historical data, you can determine which parts have failed and what circumstances were different at the time of the failure. But even if all you’re doing is comparing the current situation to your predetermined baseline, you still need to pinpoint the exact change and its location.
2. Measuring Changes in Conditions
IoT sensors are capable of detecting a wide range of environmental changes, such as variations in temperature, acoustics, speed, production, vibrations, and more. Which types of sensors you require will depend on the conditions that indicate failure; in certain cases, you may need to place sensors on various systems or components.
3. Managing IoT Maintenance Data
When it’s time to plan maintenance or repairs, these sensors send performance data to a computerized maintenance management system (CMMS), facility management software, fleet management software, or central manufacturing execution system (MES).
4. Scheduling Maintenance Service
Sometimes, you might be able to pinpoint the precise reason of a change in the environment, such a rise in temperature. However, a technician’s inspection of the equipment to identify the root cause is usually indicated by the change in condition. In either case, having extra parts and equipment on hand is essential for effective predictive maintenance because these are the items that are most likely to fail based on your historical data.
In order to minimize the disturbance to your business, you should, like with preventative maintenance, have a backup plan in place for each piece of equipment or organize your activities around the scheduled downtime.
Benefits of Predictive Maintenance
You can respond to any changes in sensor data before an asset breaks if your maintenance management system is synchronized with real-time equipment data. Stated differently, you can avert just-in-time device failure. Reliability, availability, and operational expenses all significantly improve as a result of its predictive analytics.
1. Fewer Equipment Failures
Preventing equipment failure is the ultimate objective of all maintenance and reliability professionals. Regularly checking the state of systems and equipment can cut the frequency of unexpected machine failures by over 50%. Facility and equipment managers can obtain up-to-date information about the health of their assets and take preventative measures before a failure occurs by including condition monitoring tools into their maintenance strategy. Predictive maintenance programs almost completely eliminate breakdowns by reducing unplanned failure by up to 90%.
2. Reduced MTTR
Predictive maintenance lowers the real time needed to repair or recondition plant equipment by preventing machine faults. When anomalies are identified by condition monitoring sensors, personnel can address the issues before more harm is done. By utilizing predictive maintenance, a typical facility can achieve a 60% reduction in mean time to repair (MTTR).
3. Increased Asset Lifetime
By using machine learning to identify issues with systems and equipment early on, facility machinery can have an average 30% longer service life. Organizations that use predictive maintenance techniques see a decrease in the severity of damages as well as the pace at which degradation and defects occur. This is due to the fact that a problem with a cheap part may result in damage to an important part, shortening the asset lifecycle.
4. Precise Assets Data
The capacity to forecast the mean time between failures (MTBF) using sensor data is one of the advantages of predictive maintenance. Facility and equipment maintenance managers can identify the most economical time to replace machines by using this data. By doing this, expensive maintenance procedures that won’t improve the asset’s long-term condition can be avoided.
Managers are able to determine whether maintenance and ongoing operation expenses are more than replacement prices thanks to the algorithm used by technologies such as CMMS software. That makes it simple to decide with confidence.
5. Verification of a Repair’s Efficacy
Thermal imaging, vibration analysis, oil analysis, equipment observation, and other tasks can all be carried out with predictive maintenance sensors. Before the machine restarts, PdM sensors are also utilized to confirm whether a repair was accomplished. By removing the need for extra shutdowns and repeat repairs to fix insufficient or partial repairs, this boosts efficiency.
6. Improved Workplace Safety
Maintenance managers’ top priorities are risk management and worker safety. Not only can machine failure accidents at work be extremely dangerous, but they can also result in lawsuits that have a large financial potential. Early maintenance and equipment failure detection lowers the chance of catastrophic breakdowns, preventing harm and maybe fatalities.
In reality, a number of insurance providers presently provide incentives to businesses that have a well-established program for condition-based predictive maintenance.
7. Increased ROI in Construction
In addition to the financial savings from lowering downtime, maintenance staff also save money overall by averting complicated machine malfunctions. Because they have more time to concentrate on other important maintenance duties, maintenance professionals and managers are also able to boost their productivity at work.
Maintenance managers no longer need to waste time conducting inspections or going over raw data since CMMS software uses the internet of things (IoT) to read PdM sensor data.
Disadvantages of Predictive Maintenance
Predictive approaches enable just-in-time repair to minimize unscheduled downtime and extend the life of equipment. This strategy has a lot of benefits, but there are also some drawbacks to take into account:
- High Initial Costs: Sensors, data analytics software, and occasionally even Internet of Things (IoT) infrastructure must be purchased in order to set up predictive maintenance. The startup expenses can be rather significant for many businesses.
- Complexity: Putting predictive maintenance into practice necessitates retraining staff, integrating various technologies and systems, and evaluating enormous volumes of data. This may bring in complexity, for which some organizations are not prepared.
- Over-reliance on Technology: A piece of equipment may not always function as expected, even if the system predicts it would. There’s always a chance of relying too much on predictive data and missing other indications of malfunctioning equipment.
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Predictive Maintenance Technologies
Anticipating when maintenance is required is the aim of predictive maintenance, as the name implies. Although there isn’t a Magic 8-Ball, there are a number of condition-monitoring tools and methods that can be used to accurately forecast failure and give advance notice of upcoming maintenance.
1. Infrared Thermography
Infrared (IR) thermography is a commonly used diagnostic tool in predictive maintenance that is considered nondestructive or nonintrusive. Employees can identify hotspots—high temperatures in equipment—by using infrared cameras. Damaged parts, such as electrical circuits that aren’t working properly, usually release heat, which shows up as a hotspot on a thermal picture (“Predictive Maintenance,” Lean Manufacturing Tools).
Infrared inspections can detect issues and assist prevent expensive repairs and downtime by rapidly locating hotspots. According to Control Engineering, infrared technology is “one of the most versatile predictive maintenance technologies available… used to study everything from individual machinery components to plant systems, roofs, and even entire buildings.” Infrared technology can also be used to identify thermal abnormalities and issues with industrial systems that depend on heat transmission or retention.
2. Acoustic Monitoring
Employees can use acoustic technology to find gas, liquid, or vacuum leaks in machinery at the ultrasonic or sonic levels. Though somewhat less costly than ultrasonic technology, sonic technology has limited applications on mechanical equipment. There are more uses for ultrasonic technology, and it is more accurate in identifying mechanical problems.
By using instrumentation to convert sounds in the 20–100 kHz range into “auditory or visual signals that can be heard/seen by a technician,” it enables a technician to “hear friction and stress in rotating machinery, which can predict deterioration earlier than conventional techniques” (Predictive Maintenance, Wikipedia). These high frequencies are precisely the frequencies produced by electrical equipment malfunctions, leaky valves, worn and underlubricated bearings, etc.
Although ultrasonic and sonic testing can be costly, a technician’s ears can provide an equally effective means of acoustic monitoring at a far lower cost. “Even something as basic as hearing strange noises coming from a gearbox or an oil leak can and frequently does prevent a catastrophic collapse, save tens of thousands of dollars in losses.
3. Vibration Analysis
Vibration analysis, which is mostly used for fast-rotating machinery, enables a technician to keep an eye on a machine’s vibrations using a handheld analyzer or in-the-moment sensors integrated inside the apparatus. A machine that is functioning at its best will have a specific vibration pattern. The machine will produce a distinct vibration pattern as parts like shafts and bearings start to wear out and fail. Through proactive equipment monitoring, a qualified technician can identify problem areas by comparing results to established failure modes.
Vibration analysis can identify a number of problems, including as misalignment, bent shafts, imbalanced parts, loose mechanical parts, and motor troubles. Since it might be challenging to predict machine failure using vibration analysis, it will be essential to ensure technicians are well trained. Comprehensive training is provided by numerous organizations to help people become certified vibration analysts. The cost of integrating vibration analysis into a PdM application is the only drawback to adopting it.
4. Oil Analysis
A useful technique for predictive maintenance is oil analysis. It makes it possible for a specialist to examine the oil’s state and identify any further particles or pollutants. Viscosity, the presence of water or wear metals, particle counts, and the acidity or baseness of the oil can all be determined by certain analyses.
Utilizing oil analysis has the advantage that the first test or tests will establish a baseline for a new equipment. When done correctly, oil analysis can provide a wealth of information that can support the success of predictive maintenance.
5. Additional Technologies
Facilities may also employ additional technologies in addition to these methods, such as eddy current analysis, which detects variations in tube wall thickness in centrifugal chillers and boiler systems, and motor condition analysis, which describes the functioning and running state of motors. Predictive maintenance can also be aided by CMMS, data integration, borescope inspections, and condition monitoring. While there are many technologies available to support your PdM initiatives, selecting the appropriate one is essential to their success.
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How to Get Started with Predictive Maintenance
Even though the previous part provided a general explanation of predictive maintenance, maintenance managers could still find it difficult to grasp how they can incorporate a predictive maintenance strategy into their current workflow.
1. Choose Equipment to Monitor
Predictive maintenance produces a lot of data from daily equipment monitoring, thus even though it has numerous advantages, it cannot be used on all operational equipment. It is necessary for maintenance managers to start with a small number of equipment selections to monitor in order to save organizational resources. The apparatus picked out need to:
- Have expensive upkeep
- Be vulnerable to equipment malfunctions
- Sensors can track conditions that lead to or cause failure.
2. Choose a Prediction Method
While predictive algorithms are the most well-known prediction method for predictive maintenance, they necessitate one of two actions from maintenance managers:
One of three options is available:
- Create a prediction algorithm independently;
- Commission a data scientist to do so; or
- Invest in predictive analytics software.
Predictive maintenance typically has a high entry barrier because of this. But maintenance managers also have the option of carrying out their own predictive analysis. A basic predictive analysis may assess a variety of characteristics or aspects that can help anticipate when equipment is most likely to fail, depending on the circumstances maintenance managers wish to monitor and their own preferences. A simple predictive analysis might, for instance, interpret a weekly total of equipment performance problems as an indication that the equipment is soon to break.
3. Connect Sensors to Database
Predictive analytics software can also function as a database to hold the sensor-collected equipment monitoring data if maintenance managers have made the decision to buy it. Predictive maintenance tools provide an alternate digital solution for individuals who have not acquired predictive analytics software. Numerous sensor types, including temperature, humidity, gas, air pressure, and air particle matter, can be linked to these tools.
4. Connect Sensors to Equipment
Maintenance managers can place sensors on equipment after connecting the sensors to the database of their choice. Maintenance managers should set up alerts that will tell them when equipment conditions have reached a given point after confirming that sensors are monitoring the correct equipment conditions and delivering the data to the database in real-time.
5. Schedule Maintenance
Maintenance supervisors have two options when they receive sensor alerts:
- Schedule maintenance right away or dispatch a maintenance technician;
- If you’re using a predictive algorithm for prediction, you can schedule maintenance at the time the algorithm suggests;
- If you’re using your own predictive analysis for prediction, you can compare this data with other factors and determine when to schedule maintenance.
Conclusion
With the help of data analysis, predictive maintenance (PdM) can spot possible equipment flaws and operating irregularities in advance of failures, allowing for prompt repairs. By reducing the frequency of maintenance, it seeks to reduce unplanned outages and needless preventive maintenance expenses.
With predictive maintenance, asset maintenance is scheduled optimally to minimize frequency and maximize reliability without incurring additional costs. It uses machine learning, AI, and sensor data to inform maintenance choices. Important components of a successful predictive maintenance program are equipment observation and vibration analysis. Even with drawbacks like high startup costs and specialized knowledge required, it’s effective and saves money and resources. Speak with monitoring specialists and equipment manufacturers before implementing predictive maintenance.
Over the past ten years, predictive maintenance has grown significantly in favor among facility managers as a maintenance method. Utilizing PdM, organizations can save a lot of money and benefit from more dependable and readily available equipment. When considering a predictive maintenance program for your equipment, it’s critical to first create a strategy and install the necessary sensors and technology. Measuring asset condition may seem like an expensive expenditure, but the benefits your team will experience will make it worthwhile.
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Resources:
FiixSoftware | GoFMX | Emnify | Splunk | Limble | SafetyCulture | BrightlySoftware | BuildingsIoT | AdvancedTech | SAP | UpKeep | IBM
For all the pictures: Freepik
