BigData & Analytics, IoT

How IoT & Machine learning changing the face of Predictive Maintenance.

When you talk about machines or manufacturing, maintenance comes buzzing around. Whether you buy a car, a household utility or whether it...

mm Written by Emorphis Technologies · 4 min read >
Ai and Machine learning

When you talk about machines or manufacturing, maintenance comes buzzing around. Whether you buy a car, a household utility or whether it is any machine at the manufacturing unit, all require maintenance. The reason is all the machines; no matter its place of use, can break in the long run. In fact, a study by Wall Street Journal & Emerson found that industrial manufacturers have faced an unplanned downtime and 42% of times it is due to equipment failure.

What can be done to prevent this substantial loss? The answer to this is quite simple- engage in an effective machine maintenance strategy.

During the early days, machines were not too complex and hence there were limited breakdowns. However, with the advancements in machines through program and logic controllers the scenario of fewer breakdowns has changed. Previously, there was more of maintenance through manual labour on contrary to what it is now. The manufacturing units and factories want to stay competitive by rapid assembly line and tremendous automation through complex machines. This assist in measuring performance metrics such as production efficiency, output, and equipment efficiency. Because of all this, the “maintenance” which was done only during equipment failure has now become a routine scheduled activity known as Preventive Maintenance.

Understanding the Difference –“Preventive Vs Predictive” Maintenance

Though, many confuse between Preventive and Predictive Maintenance. Let us understand the difference with an example. You bought a car and the car-manual says you need to change oil after every 1000 kms or in 2 months’ time. You then take your car to the shop for oil change so that your car stays in a good condition. This is called Preventive maintenance.

Now, this time you bought a car well equipped with all the indicating devices and air filters and all the accessories it can have. You drive this car for 500 kms and then the alert comes in car that you have 500 kms left to change oil in car.  This is called Predictive Maintenance. Such maintenance prevents machines from breakdown and gives a prior alert to repair before the machine could actually fail.

Preventive maintenance has been very popular with manufacturing industries so far, but Predictive maintenance delivers a different value to industry. Preventive maintenance is planned and scheduled like in case of car. In actual your machine does not require fixes based on its current state, but since it is scheduled, you are bound to go for maintenance. On contrary, predictive maintenance is based on actual condition of machine instead of scheduled time. This allows companies to predict equipment failure before they could occur and provides enough time to schedule future maintenance.

The 4th wave industrial revolution has changed the way maintenance have been functioning. With the introduction of technologies Internet 4.0 or Industrial Internet Of Things, the equipment’s are now connected with sensors that utilize advanced analytics and machine learning to draw significant comprehensions. Based on these data insights, maintenance is carried out in any machine. This maintenance strategy that utilize machine learning analytics is known as Predictive Maintenance, that results in substantial savings for companies.

Anomaly Detection in Predictive Maintenance

With machine learning and IIoT gearing up in manufacturing industry, these has been a data explosion with tons of data in form of sensor data, social media data, CRM data, web data, clinical & system data and many more. This massive amount of data go through evaluation to predict behaviours and events, quality control, cost optimisation, and productivity calculation etc. But what comes as a challenge here is the prediction of “something”. This something is “Anomaly”. Anomaly is an affair that is not associated with the system’s past neither it is in the historical data. Anomaly is nothing but an unexpected behaviour or a change in any process of system due to some internal events. This information about the unexpected or unusual behaviour is called as Anomaly.

Anomaly detection makes predictive maintenance effective. It is a method that determine any unusual pattern or activity that could result in system failure. For example, a mechanical system contains temperature, vibration and speed sensors. In case the gear box is about to fail, these sensors would provide a threshold value that can alert about the gearbox breakdown. But, with anomaly detection you can make use of the data from available sensors and decide for an inspection even before the threshold time. This would help in taking measures to prevent gearbox failure.

You may also like reading – What is IIOT

How machine learning/ IoT is a game changer in predictive maintenance

With machine learning and Internet of Things making its way through all the areas of industries, predictive maintenance is no left behind. For industries, unexpected and unnecessary downtime is the biggest issue. Hence these industries realise the importance of identifying the potential failures, their occurrence and consequence. To cater to this problem, organisations now use machine learning for faster and smarter data driven decisions.

Machine learning has been in place for quite some time now, but its capacity to utilize artificial intelligence with the big data in industrial infrastructure is now advancing rapidly.

Companies have been using data received from visual inspections, instrument inspection, and existing condition monitoring to perform maintenance. However, with machine learning, it becomes easy to identify patterns in available data and predict machine outcome. This is also known as predictive analytics. It works by identifying the correct data set and combine it with machine to feed real-time data and improving data quality by tracking machines for failures. For accurate predictive maintenance, data quality is of prime importance.

For predictive maintenance, advance machine learning algorithms are applied to sensors that are integrated with machines. These algorithms link sensors to huge amount of data in real-time. Sensors with the use of this data, are able to monitor equipment condition and predict any anomalies that could lead to failure.

With Predictive maintenance, manufacturers can lower the maintenance costs, lessen the downtime and extend equipment life, thereby enhancing quality of production by attending to problems before equipment fails. Though, to utilize these benefits, a flexible digital system must be set up which includes an IoT platform such as Microsoft Azure IoT Suite and using statistical methods such as predictive modelling and machine learning to analyse machine data.

The Way Forward: Predictive Maintenance 4.0 is the future

The internet 4.0 wave has positively hit the predictive maintenance area of industrial domain. Machine learning combined and embedded with IoT application development & AI applications, will assist organisations in managing, monitoring and maintaining the condition of their equipment. By deploying these machine learning and AI enabled smart solutions, companies can reduce the need for manual checks, save cost and ample amount of time.

Sensors embedded with machine learning technology can deliver useful decision making insights for the staff to predict machine failure and they can act fast before it crush down.

Preventive Maintenance 4.0 is also helpful in managing Key Performance Indicators at an industrial unit, for effective health and safety measures. By monitoring and acting well upon the data flow from connected equipment & manpower, it is easier to identify potential faults and prevent injuries & downtime.

The smart collaboration of machine learning and big data analytics have improved predictive maintenance decisions by its faster, intelligent and responsive models.