All businesses that own assets like computers, servers, pipelines, or railroad tracks face the same challenge: how can these assets be kept functioning for as long as possible and as economically as possible without sacrificing dependability or safety? To address these difficulties, predictive maintenance is a strategy that employs data analysis tools and methodologies in order to find abnormalities in your operation and probable defects in your equipment and processes before they lead to a breakdown. In this blog, we will discuss what predictive maintenance is, its advantages, and everything else you’ll need to know to implement predictive maintenance.
What is Predictive Maintenance?
Predictive maintenance is a proactive maintenance method that continuously analyzes an asset’s performance and condition in order to predict when maintenance is necessary before an asset malfunctions. A type of condition-based maintenance called predictive maintenance (PdM) uses sensor devices to keep an eye on the health of assets. Real-time data from such sensor devices are utilized to forecast when an asset will need repair and, as a result, avoid equipment failure. The most cutting-edge form of maintenance currently available is predictive maintenance. Organizations that use time-based maintenance run the risk of either executing too little or too much maintenance. Also, with a reactive maintenance process, repairs are made as needed at the expense of unplanned downtime. Such problems are resolved with predictive maintenance.
Predictive maintenance seeks to identify prospective equipment issues as early as feasible in order to prevent the need for more involved maintenance procedures. Utilizing condition-monitoring techniques, predictive maintenance tracks the performance of assets during normal usage, identifies any flaws, and corrects them before the asset fails. Furthermore, the reduction of machine downtime is linked to significant cost savings with predictive maintenance programmes. Businesses that invest in PdM considerably enhance return on investment while reducing maintenance expenses by up to 30%.
Features of Predictive Maintenance
Monitoring & Tracking Assets
Every resource is significant to the organization. Therefore, it is crucial to keep track of its assets, particularly when those assets are expensive and necessary for day-to-day operations. Additionally, by keeping track of your item, you may gather important data about it, such as how frequently and for how long it is used, who has it, when it will be returned, etc.
Reports & Insights
Reports are quite useful for pinpointing problems that are happening with each asset separately. Reports offer data, and data is essential for business expansion. Additionally, reports contain valuable data that aids in identifying areas for improvement. Reports are the source where you can see all the information about how to budget money for your business, what areas you want to strengthen, etc. Statistics and analytics reports eliminate the need for conjecture. Reports from a predictive maintenance system allow you to make precise and informed decisions that will immediately and significantly impact your organization.
Work Order Management
A work order is created whenever a maintenance operation needs to be performed. It enables the organization’s maintenance managers to monitor each work order and its status so they may update the end-user directly on its progress. Managers can keep an eye on asset performance using the work order management function. Also, through the analytics feature of the predictive maintenance system, they can learn crucial information about assets.
Types of Predictive Maintenance
Predictive maintenance can be used for several processes. Although there are different types of predictive maintenance, the goal is always the same, so let’s get into more depth about them:
Vibration Analysis: Vibration analysis is a method for tracking changes in a machine’s vibrations. The AI system establishes standard vibration levels and only notifies the management if a machine deviates from those values. They also aid in the detection of loose equipment parts, misalignment, imbalance, and bearing wear and tear and are utilized for high-rotating machinery in industrial plants.
Acoustic Analysis: AI in business has advanced to the point where it can recognise errors even through sound. By listening to a machine’s internal workings, the system can identify which machine has a problem, much like an experienced worker. However, if the place is noisy and full of ambient noise, this approach is useless. Similar to vibrational analysis but with a focus on preventative lubrication techniques, acoustic analysis is employed for both low and high rotating machinery.
Infrared Analysis: By monitoring the temperature, predictive maintenance systems can also identify which machines are malfunctioning. The system can locate damaged electrical circuits, identify hot spots in electronic devices, determine the fuses under stress, etc. An infrared examination is the most economical approach for preventive maintenance when the temperature is a reliable predictor of possible problems. It is frequently used to detect issues with cooling, airflow, and even motor stress.
Why is Predictive Maintenance Crucial for Businesses?
The future and business revolution are already here with predictive maintenance. And it’s not just a theory being debated in academic settings; some of the biggest businesses in the world in manufacturing, aviation, and healthcare are seeing significant increases in productivity, efficiency, and savings as an outcome of their predictive maintenance programs. According to a survey by Market Research Future, the global market for predictive maintenance will grow to $6.3 billion by 2022. Additionally, according to the report, the Predictive Maintenance (PdM) Market will reach USD 111.34 billion by 2030, increasing at a CAGR of 26.2% from 2021 to 2030. Predictive maintenance, which ensures that tasks are carried out precisely at the right time, has now evolved into maintenance’s fundamental basis.
With PdM, there is no risk of completing too much or too little maintenance work, unlike preventative maintenance. According to the U.S. Department of Energy, a functioning predictive maintenance program can enhance ROI up to ten times. Additionally, it can cut down on asset breakdowns by 75%. In turn, this results in a 45 percent decrease in downtime and a 30 percent decrease in maintenance expenses.
How does Predictive Maintenance Work?
Predictive maintenance’s key benefit is the ability to plan work based on the asset’s existing state. However, it is anything but simple to determine the precise state of complex assets. PdM can track asset conditions and alert technicians to impending equipment breakdowns according to three key elements such as,
Condition Monitoring Sensors: These sensors gather information about the asset and work with software to detect wear and tear immediately. Temperature, pressure, vibration, noise, oil, corrosion, and electrical current levels are just a few of the equipment factors that sensors can keep an eye on. Vibration analysis instruments, ultrasonic technology, and infrared thermography cameras are examples of advanced substitutes. In other words, condition monitoring sensors are utilized to gather data about an equipment’s performance. It is possible to collect data on temperature, vibration, noise, and pressure, depending on the type of sensor you utilize.
Internet of Things (IoT): IoT aids in the analysis and interpretation of equipment data.
Using IoT technology, the many sensors gather and exchange data with a centralized information system. Furthermore, IoT technology makes it possible for machines, software programs, and cloud computing to communicate with one another, effectively facilitating the collection and analysis of enormous volumes of data.
Predictive Models: In order to determine when a piece of equipment requires maintenance, upkeep, or replacement, predictive models look for trends. The calculations rely on established algorithms that contrast the actual behaviour of a machine with its predicted behaviour. Deviations are signs of slow deterioration, and your maintenance staff can take prompt action to prevent equipment failure by identifying these deviations early on. All of that processed data is loaded into predictive data models so they can determine failure predictions.
How to Implement Predictive Maintenance?
You must first compile a list of your company’s important assets before you implement a predictive maintenance strategy on them. Create a baseline of all the equipment that has been identified after that, and periodically check on each piece of equipment to determine its condition. If equipment is not constantly monitored, it is impossible to tell whether it might be malfunctioning. Here are some more steps to assist you in starting to implement predictive maintenance in your organization after determining your most valuable assets and machinery:
Step 1: Securing the Budget
Predictive maintenance implementation is a process that takes time. You must obtain top management approval and assurances that the project will be adequately funded before making any arrangements.
Step 2: Identify crucial assets
Start by identifying key assets to be included in the PdM programme. The greatest prospects are frequently those assets with high repair/replacement costs that are essential to production. To determine whether an asset is worthwhile for inclusion in a predictive maintenance programme, you can also conduct an RCM analysis. Furthermore, utilize historical data to evaluate your equipment— Gather the data for future incident tracking or historical analysis from sources like maintenance records, CMMS, or data that your machinery already produces. You can take action based on the information provided by this data, which will also serve as a foundation for the predictive maintenance program.
Step 3: Create a database
Another aspect to take into account for the PdM program’s success is the availability of sufficient data that may provide practical insights into machine behaviour. A variety of sources, including CMMS, hard copy files, enterprise applications from other departments, maintenance records and charts, specialists’ personal experience working on the assets, etc., will provide historical data for each piece of pilot equipment. This information can be utilized to identify failure modes and may even be helpful when creating the initial prediction algorithm.
Step 4: Analyze Failure Modes of Assets
To determine the failure modes of the previously identified important assets, the company will now need to do an analysis on them. Using this technique, they can determine which failures are the most problematic and have the greatest likelihood of occurring.
Step 5: Implement sensors and tools for condition monitoring
The company can purchase suitable sensors and technology to monitor the parts that are most likely to break once they are aware of the failure modes they need to be on the lookout for. As we just covered in one of the sections of this article, there are different condition monitoring approaches and tools available. Old assets should be avoided since it may be difficult to equip them with contemporary sensors.
Step 6: Build predictive models
After putting everything else in place, the next stage is establishing the appropriate modelling strategy that will serve as the foundation for failure predictions. In order to create predictive maintenance models based on the sensor measurements and other data, organizations can hire data scientists to seamlessly make predictions or forecasts on the available data.
Step 7: Deploy to pilot equipment
Predictive modelling is put to the test and validated at this point by applying the technique to a particular subset of pilot equipment. The company’s operations will significantly improve if the process is carried out correctly. Depending on the size of business operations or how much machine downtime was encountered prior to the installation, noticeable effects could not be felt for a few months.
Benefits of Predictive Maintenance
Lower Rates of Equipment Failure
Every maintenance department should avoid equipment failure because it can have adverse effects. Regularly checking on the equipment and process systems can cut the number of unexpected machine breakdowns by more than 50%. Facility managers can receive real-time information about the condition of an asset and take the appropriate action before a failure occurs by using a condition monitoring maintenance technique. A predictive maintenance program can almost completely eliminate breakdowns by reducing unexpected failure by up to 90%.
Prolonged Asset Life
The average service life of facility machinery is increased by 30% by using machine learning to identify machine and system issues early. Organizations lower both the degree of damages and the spread of flaws when using a predictive maintenance approach. This is due to the possibility that a problem with a cheap component could result in damage to a crucial component, shortening the asset lifecycle.
Enhanced Workplace Security
Facility management’s top priorities are risk control and workplace safety. Machine failure-related workplace accidents can result in litigation with a significant financial impact in addition to being harmful. Early discovery of maintenance and equipment issues lowers the possibility of catastrophic failures, preventing damage and even death. In fact, a number of insurance providers now reduce benefits for buildings that have a functioning condition-based preventative maintenance programme.
Validating the Effectiveness of Repairs
Sensors for predictive maintenance can be utilized for a variety of tasks, including vibration monitoring, oil analysis, thermal imaging, and equipment observation. Additionally, PdM sensors are employed before a machine restarts to determine whether a repair was effective. This improves security and prevents the need for a second shutdown, which is sometimes required to correct insufficient or unfinished repairs.
Maintenance staff can spend less on tools and services by preventing complicated machine faults. As they have enough time to concentrate on crucial maintenance chores, maintenance professionals and supervisors are also able to boost work productivity. With CMMS software that retrieves PdM sensor data from the internet of things (IoT), maintenance managers no longer have to waste time reading over work order data analyses. Additionally, improving the conditions of the equipment lowers machine breakdown, which directly impacts the bottom line.