One of the major use cases of industrial IoT is predictive maintenance that continuously monitors the condition and performance of equipment during normal operation and predict future equipment failure based on previous equipment failure and maintenance history. With an accurate equipment failure prediction organisations can reduce cost from unplanned breakdown and unnecessary preventive maintenance. Driven by the temptation of large cost saving many organisations are interested to deploy their predictive maintenance solutions.

When starting a predictive maintenance project a number of questions need to be raised to the business to help making the solution design decision.

Firstly, we need to know what type of prediction the organisation is aiming at. There are three types of prediction we can normally do for predictive maintenance:

RUL (Remaining Useful Life)  – This is a regression type prediction that estimates the remaining usable time of an equipment before it runs to a failure. This type of prediction is suitable for equipment that does not run in a fixed time pattern.

Failure within next period – This is a two-class classification type prediction that estimates whether or not the equipment will fail within the next period (e.g., next week). This type of prediction can alert the engineers the potential failure for them to arrange maintenance in time to avoid the failure.

Failure within which next period – This is a multi-class classification type prediction. Instead of predicting weather the equipment will fail within the next period it estimates within which of the next period (e.g., next week, next bi-week, or next month) the equipment will fail.

Secondly, we need to ask what the time window (e.g., hour, day, or week) is to use for the prediciton. The reason to ask this question is to help us decide the granularity of the training dataset. Depending on the type of equipment and the way they use, some equipment failures may be predicted weeks before they happen, but some failures can only be predicted hours before. Therefore, we need to choose the suitable level of granularity of the time windows and aggregate the raw per sensor reading data accordingly.

Based on the answers to the first two questions we can work out a list of pre-requisites for the predictive maintenance solution. Some history data has to be available before we can train the predictive model, for example:

• History data of equipment states (e.g., the measurement values of the components and unusual events such as liquid leaks)
• Equipment reference data (e.g., the normal value range of a component state such as the min and max level of temperature in normal condition). We need the reference data to extract the exception states of the equipment that may contribute to the predictive model
• Equipment failure history. This is the necessary data for predictive maintenance modelling, otherwise we cannot establish the relationship between the equipment states and the failure event.
• Equipment maintenance history. We need to know how long since the machine is lat maintained that can be an important predictor for the potential failure. In addition the frequencies of equipment maintenance can be a candidate indicator of the health status of the equipment.

The missing of necessary history data can be the game-killer. If that happens we need to go back to the square one and start to systematically plan the data collection.

• Machine Learning / Data Mining