IoT to reduce Breakdown Time

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.
Previously the machines were less complex in nature and hence the breakdown were restricted to small amount of time.But now breakdown is a frequent problem due to advancement in machines through programs and with introduction of complex logic controllers.
With the heavy reliance of companies on these technologies, facilities have used IoT for efficient maintenance policies. Machine-integrated sensors tasked with collecting data sets and sending them through secure pathways to cloud-based management platforms provide a more holistic picture of machine health. Operators can apply the gathered usage information from the sensors to predict and implement effective servicing cycles, greatly reducing overall machine downtime. The impact of predictive maintenance, which we have discussed in detail in the later part already, has proved significant in streamlining operations and reducing costly downtime states. As per the report from McKinsey & Company, IoT-enabled predictive maintenance could potentially save manufacturers $200 billion to $600 billion by 2025.
Now to prevent these types of machine failures we should have a proper 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 labor on contrary to what it’s now. The manufacturing units and factories want to remain competitive by rapid production line and tremendous automation through complex machines. This assist in measuring performance metrics like production efficiency, output, and equipment efficiency. Because of all this, the “maintenance” which was done only during breakdown has now become a routine scheduled activity referred to as Preventive Maintenance.
Now let us look at the difference between Preventive And Predictive maintenance.
To understand this let us take an example of a car. Suppose you bought a new car then the car manual reads that you have to change your brake oil after 100 kms and you change it accordingly. Then this is preventive maintenance.
Now if the car itself sends an alert after say 80 kms saying that you have to change oil and you have 20 kms left then this is Predictive maintenance.
Preventive maintenance has been very fashionable with manufacturing industries thus far , but Predictive maintenance delivers a special value to industry. Preventive maintenance is planned and scheduled like in case of car. In actual your machine doesn’t require fixes supported its current state, but since it’s scheduled, you’re sure to choose maintenance. On contrary, predictive maintenance is predicated on actual condition of machine rather than scheduled time. This allows companies to predict breakdown before they might occur and provides enough time to schedule future maintenance.
The 4th wave technological revolution has changed the way maintenance are 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 knowledge undergo evaluation to predict behaviours and events, internal control , cost optimisation, and productivity calculation etc. But what comes as a challenge here is that the prediction of “something”. This something is “Anomaly”. Anomaly is a phenomenon that is not associated with the system’s previous historical data. Anomaly is an unexpected behaviour or a change in any process of system due to some internal issues. This phenomenon about the unexpected or unusual behaviour is called as an Anomaly.
Anomaly detection makes predictive maintenance possible. It is a process that determines any abnormal pattern or activity that has the probability of system failure. For example, a system contains temperature, vibration and speed sensors. In case the engine is about to fail, these sensors would provide a threshold value of temperature or vibrations that can alert about the engine breakdown. But, with anomaly detection you’ll make use of the info from available sensors and choose for an inspection even before the edge time. This would help in taking measures to prevent engine failure.

The Way Forward
The internet 4.0 wave has hit the predictive maintenance area of industrial domain in a positive approach. Machine learning combined and embedded with IoT application development & AI applications, will assist organisations in managing, monitoring and maintaining the behaviour and thus in turn 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 a huge amount of amount of time.
Sensors embedded with machine learning technology can deliver useful deciding insights for the staff to predict machine failure and that 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 large data analytics have improved predictive maintenance decisions by its faster, intelligent and responsive models.