Prevention is better than cure. This phrase also applies to machines in manufacturing. If a machine that produces tons of end-products every day breaks down, it may lead to significant business loss. Manufacturing unit breakdowns can also create a gap between the demand and supply of the goods which may cause supply chain disruption.
Global manufacturing leaders in the nineties came up with the term predictive maintenance which involved monitoring and scrutiny of the equipment during daily operations rather than waiting for a scheduled maintenance or a complete breakdown. The monitoring was carried out manually as digital tools were not yet discovered. But today, manufacturers have undergone a massive transformation, leveraging digital tools such as AI, ML, automation, etc. to predict machine health with more precision.
What is Predictive Maintenance?
Predictive maintenance is a proactive maintenance strategy that uses monitoring tools to detect anomalies, deterioration signs, and equipment issues. With these signals, manufacturers can run predictive algorithms to predict and estimate when a piece of equipment might fail so that maintenance or repairs can be performed before it happens.
Predictive maintenance has changed over the years. Manual monitoring has been replaced by advanced technologies such as vibration monitoring, thermography, oil analysis, ultrasonic, and emission testing.
According to a predictive maintenance report by Market Research Future, the global predictive maintenance market is expected to grow to USD 23 billion by 2025. The goal is to schedule maintenance at the most convenient and most cost-efficient moment, allowing equipment’s lifespan to be optimized to its fullest and getting the most RoI.
Predictive maintenance technologies include non-destructive monitoring methods involving visual, sound, and thermal imaging sensors. These sensors include acoustic, infrared, liquid analysis, sound measurements, vibration analysis, and thermal imaging. These sensors help to gather real-time data at regular intervals via wireless networks which is stored and analyzed by pre-built algorithms to detect anomaly.
Is Preventive and Predictive Maintenance the same?
Although both preventative and predictive maintenance fall under the umbrella of planned maintenance, they do have very distinct differences that make them unique in their own way. Preventive maintenance relies on historical data to determine the life of a system or unit and perform scheduled maintenance at regular intervals to ensure longevity.
Predictive maintenance, on the other hand, relies on real-time data of the machine to understand the present wear-and-tear, and suggest a fix basis the data and analysis.
Think of it this way: Let’s say you have a 300-meter long conveyer belt that carries potatoes from a washer to the dryer. The company that manufactured it will give you a scheduled maintenance brochure which will tell you the optimum interval to perform the maintenance to derive optimum output. Now, basis this, you will carry out all maintenance at the exact interval regardless of the condition of the machine, weather, geography, etc. Which means, if the weather around is too humid and the machine runs even 1 degree hotter than normal, its wear-and-tear will happen much faster than its estimated time and the system might breakdown before the scheduled maintenance.
Preventive maintenance can’t detect this. It can only carry out preventive maintenance based on historic data, but predictive maintenance will leverage tools that will specifically monitor your machine and identify the temperature change to predict and send an early warning signal to you.
There are tools that can even monitor every nut and bolt in the machine so that they can be changed without having to replace the machine itself. It helps manufacturers to save on regular maintenance costs when its redundant and also reduces unnecessary downtime.
Let’s look at some predictive maintenance tools that has transformed the manufacturing industry.
1. Acoustic Monitoring: Acoustic monitoring detects the sounds of gas emission, liquid, or vacuum leaks in equipment at the sonic and ultrasonic level. In ultrasonic analysis (UA), sensitive microphones take high-frequency sounds and turn them into audio and digital data that can be analyzed by computers and humans. Regular UA data is compared with past data to track irregularity and detect potential issues. UA units include onboard thermometers, cameras, and spectral analyzers for audio capturing, tracking, and data analysis.
2. Infrared Thermography: This technology uses infrared cameras to detect change in temperature in an equipment and identify patterns to trigger warning signals. The tool uses Infrared (IR) blasters to detect a change in temperature. A change in oil pressure, motor rpm, turbine motion, or room temperature can cause a rise or dip in the temperature of the equipment which will be detected in a fraction of a second by thermal imaging that sends out an anomaly warning to the resident personnel.
3. Oil Analysis: Technicians can establish the presence of contaminants by checking oil conditions. A sample of oil inside an equipment is tested for its viscosity, water, particle count and other wear indicators. Manufacturers can also detect metal wear-and-tear if particles of metal are detected in the samples. Oil samples are tested via advanced tools that compare previous samples with the new one to detect deflection or change in parameters. This data is then sent to laboratories for further analysis and further actions such as oil change or unit repair is recommended.
The advancement in technology such as the Internet of Things (IoT), artificial intelligence (AI), augmented and virtual reality (AR/VR), among others have shaped the maintenance strategies in the manufacturing industry. Machine learning and pattern recognition platforms have improved decision-making. They have also helped in extracting more data from various touchpoints to analyze and drill-down deeper into equipment and enhance longevity. Organizations that do not adopt predictive maintenance strategies are likely to fall behind or lose out on a significant competitive advantage. Predictive maintenance tools will enable manufacturers to optimize their supply chain, have a substantial impact across the value chain, and make them ready for Industry 4.0.