The complete guide to Predictive Maintenance
Every maintenance manager would like to predict when a breakdown will occur. But how? The IoT lends a hand.
Using sensors and new technologies that monitor signs of deterioration and changes in the machine’s performance, managers can estimate when a failure will occur.
And so a new type of maintenance is born, predictive maintenance.
What is predictive maintenance?
Predictive maintenance is a proactive maintenance strategy that aims to prevent breakdowns. Depending on the data collected and predefined predictive algorithms, it tries to estimate when a breakdown will occur. Maintenance activities are then scheduled based on these forecasts.
How does predictive maintenance work?
In theory, it is easy to understand how predictive maintenance works. It gathers data about your assets and, from that, extracts information that allows you to calculate when you need to do maintenance.
In practice, we can divide the process into 3 stages:
1. Data Collection
As we have already seen, the idea is to predict when a breakdown will occur. As this depends on obtaining quality data, the first step is to install sensors capable of collecting information in real-time on the performance and ‘health’ of the equipment.
The data that these sensors need to measure and collect depends on the techniques you intend to use to monitor the equipment. You can control vibration, temperature, pressure, noise level, or corrosion levels, among others, depending on which is best for your equipment. We will explore several predictive maintenance tools in a moment.
2. Data mining
Accumulating data about your assets is of no use if you don’t know how to exploit them. It’s the Internet of Things (IoT) that allows sensors to send all information to a central system or software that allows you to analyse what is going on. Predictive maintenance is much more effective, and much more surgical, in systems where the various assets are integrated.
3. Calculations and Machine Learning
Some people may believe that predictive maintenance stops here. But if you are only acting when the sensors detect anomalies, you are just performing condition-based maintenance – as we saw above. The most differentiating component of predictive maintenance is to build and apply algorithms that offer a prognosis.
In the beginning, it can be based on equipment history, maintenance logs, and statistics (your CMMS reports are extremely useful in this step). However, as Artificial Intelligence becomes increasingly sophisticated, it is possible to detect anomalies even earlier, find correlations and receive intelligent suggestions to prevent a breakdown. This intelligent maintenance is giving rise to a new type of maintenance, prescriptive maintenance.
Predictive Maintenance Tools, Applications and Examples
The idea is planning maintenance according to the data. That’s why non-destructive tests (NDT) are so important to diagnose failures within the infrastructure. NDTs do not compromise equipment and can be performed while it’s running, just like routine check-ups for humans. Blood tests, ultrasound scans, or eye exams: we do them every year, and none of them hurt us.
Among the predictive maintenance techniques used in the industry are:
- Vibration analysis;
- Oil analysis;
- Acoustics;
- Infrared or thermal imaging tests (thermography);
- Motor circuit analysis.
Thermographic analysis and infrared analysis
- applications: electrical connections and systems, heating systems, fluid analysis, discharge patterns, roof maintenance
Thermographic analysis can also be applied to several types of equipment to detect wear, rusting, delaminations, and disconnections that might go unnoticed to the naked eye.
The advantage of infrared is that it allows you to make temperature maps, even from a distance. This technique is used, for example, in the maintenance of heating systems. Temperature variations are useful for assessing the condition of motors and mechanical and electrical components, the building insulation, and even the condition of solar panels. The major disadvantage is that it cannot be used on reflective surfaces.
In the future, CPU temperature analysis will be one of the main ways to test the health of robots.
Motor circuit analysis
- applications: assess engine degradation, shaft and rotor alignment, insulation, gears, scan for short-circuits
Motor circuit analysis uses a technique known as electric signature analysis (ESA), to find anomalies in electric motors. In addition to analysing the circuit and its components, it evaluates the voltage and current entering the motor. Not least important: it works on both AC and DC motors and can be used while the equipment is running.
Oil analysis
- applications: turbines, hydraulic and electro-hydraulic systems, evaluate engines, transmissions, gears, lubricant levels
The purpose of oil analysis is to test the viscosity, the amount of water and the presence of other materials, including metals, to determine the wear of the equipment.
Let’s take into consideration a hydraulic system that consists of two essential components – the rotating parts and their lubricant. As equipment ages, the sample will show byproducts of overheating and erosion. Different particles reveal many problems before you can guess a breakdown is coming.
Different particles reveal different damages to different components. For example, the presence of silicone above 15 ppm can indicate that the insulation is wearing out, while sodium can reveal contamination with salt water. Follow a metal guide (page 71) when using the spectrometer.
Vibration analysis
- applications: test component alignment, detect imbalances, clearances, resonances, gear failures
Vibration analysis is perfect for rotating equipment and machines, such as compressors, water pumps, and engines. Thus, this predictive maintenance technique is ideal for infrastructures with a complex water supply system, such as hotels, spas, or water parks.
What we do is connect them to a sensor that can detect movement or acceleration, depending on what’s appropriate. The sensor works by detecting sound waves created by movement, which generate electrical impulses and make equipment vibrate.
Acoustic analysis
- applications: pipes and plumbing, condensers, vacuum systems, fans, air compressors
We’ve already established that sound waves are our allies. Acoustic analysis is a technique used to detect problems in the material’s technical performance, pinpoint the source of the problem, and perform “check-ups” on the equipment’s overall health. How? By detecting changes in sound frequencies.
Every working machine makes some sort of noise, but its frequency and range change whenever there are leaks or pressure changes, for example. This predictive maintenance technique is especially useful for pipelines carrying liquids or gas. Some more modern tools incorporate thermometers and cameras to deepen the analysis at a distance.
Comments