Predictive maintenance allows operators and users of industrial infrastructures and rail networks to obtain real-time information on their assets.
The aim is to stay one step ahead of the processing of maintenance operations.
Predictive maintenance allows data to be recovered from existing equipment and measuring instruments using connected sensors.
The data is then unified in order to be able to analyse it and reveal weak signals that will turn out to be indicators of premature wear or future failures.
In other words, it’s about being able to anticipate anomalies and equipment failures.
To implement this type of solution, it is possible to use sensors natively equipped with IoT connectivity or to equip unconnected measuring instruments with IoT modems.
Predictive maintenance is opposed to corrective maintenance, which is based on a model of repairing and replacing defective elements .
With corrective maintenance, the asset’s breakdown can interrupt production during the length of the repair, reducing output and having a significant impact on its service rate.
Predictive maintenance is also opposed to preventive maintenance. Preventive maintenance requires additional work by operators to identify a failure model by comparing the equipment’s behaviour with a business reference system.
The predictive maintenance market has a bright future ahead by improving operational efficiency and being by far more cost-effective in many different applications.
The McKinsey Global Institute study « Unlocking the potential of the Internet of Things » estimates that predictive maintenance could save nearly €630 billion by 2025 with the Internet of Things .
The IoT based predictive maintenance can therefore reduce equipment downtime by up to 50% and reduce equipment capital investment by 3 to 5% extending the operating life of the machines.
Investments in predictive maintenance reached $9.1 billion in 2016 worldwide, according to ABI Research.
These substantial investments reflect a massive adoption of predictive maintenance concepts but are not yet enough .
The real growth of these solutions will be possible thanks to the massification of deployed solutions and data collected, and by the use of increasingly sophisticated tools on data processing.
By using AI-based analytical models and machine learning algorithms, business processes can be continuously improved. Only then the probability of failures will be predicted before they occur .
We’re still a few years away before predictive maintenance reaches its full potential. But a real revolution concerning maintenance is already underway!