We live in a world full of mechanical components and machines that we use day by day. All machines run to failure at some point. An unexpected breakage of domestic appliances affects your mood or a monthly budget, while any failure on manufacture can cost millions of dollars.
Every company needs to control the quality of equipment and organize regular maintenance. It can reduce the probability of failures and possible losses for a company. One of the best ways to predict losses is to implement machine learning methods for predictive maintenance.
This article may be relevant to Data Analysts and specialists within the Machinery industry.
Predictive maintenance is a technique of continuous monitoring of equipment performance and defining its condition. Such monitoring during normal operations helps to detect possible defects and successfully predict failure in advance. The main goal of predictive maintenance is to reduce unplanned glitches or downtime of equipment.
Predictive maintenance makes it possible to conduct timely technical support. This approach involves periodic inspections and repairs of defects with the goal of avoiding equipment malfunction or failure.
As for any data analytics, you will need historical data and ML algorithms to organize predictive maintenance. The success of predictive maintenance models depends on three main components: having the right data available, framing the problem appropriately, and evaluating the predictions properly.
Predictive maintenance can be done by utilizing the process of anomaly detection. Algorithms show you any abnormal behavior of the equipment, thus, you will be able to detect and resolve any encountered issues. As well as monitoring the ongoing processes, you can identify if there's a possibility of future failures. In this case, you will need equipment and its behavior metrics. A company will be able to prototype their future condition.
There are the following approaches for predictive maintenance:
Regression models to predict remaining useful lifetime (RUL)
Remaining useful lifetime (RUL) is the length of time equipment is predicted to operate before it requires repair or replacement. Regression models for the RUL require available static and historical data with labeled events.
The data that can be used to determine RUL is the following:
As a result, you will have information about how many days or cycles are left before the system fails.
Classification models to predict failure within a given period
If you decide to create a model that can predict lifetimes very accurately, it can be very challenging and time-consuming. However, usually, you don't need to be very precise when it comes to failure prediction. The information that "the equipment is going to fail soon" often is enough. As with the regression models for RUL, you will need both static and historical data with all events labeled. In fact, regression and classification models are pretty similar, but mostly differs on:
As a result of such analysis, you will know if a machine fails in the next period of time.
Detecting anomalous behavior approach
In some cases, analysis of the failures doesn't work. In lots of different industries, the is very little information about failures or no examples at all. For example, you can't afford to collect data about airplanes or railways crashes, as it's a matter of not only money but potentially human lives. For such cases, you need to monitor the behavior of the vehicle or a machine to figure out if the shown behavior is normal or not.
For such analysis, you will need static and historical data, but there may be labels that are unknown or too few failure events were observed or there are too many types of failure.
It is possible to define what normal behavior is and the difference between current and "normal" behavior is related to degradation leading to failure.
The anomaly detection model is very controversial because of its generality. The model should be able to detect all kinds of failure, even in cases with no previous information about them. At the same time, abnormal behavior doesn't necessarily fail. In cases, where it can lead to failure, the model doesn't provide the information about possible time frames when it might occur.
The analysis of an anomaly detection model is also challenging because of the lack of labeled data. When no labeled data is available, the model is usually made accessible and domain experts provide feedback on the quality of its anomaly flagging ability.
Survival models for the prediction of failure probability over time
A survival model evaluates the probability of failure for a specific type of equipment with static features, as well as it shows the impact of certain features on a lifetime. It provides, therefore, estimation for a type of machine with similar characteristics.
This strategy gives an analysis of how the risk of failure can change in time with the set characteristics.
Predictive maintenance is a beneficial approach for many manufacturers. Although it requires organizational changes, in some cases large investments in hardware, proper software implementation, predictive maintenance can bring a company huge value:
The financial impact of successfully implemented predictive maintenance mechanisms can reach millions of dollars depending on the business size and the use case. Machine learning methods in predictive maintenance can provide a much more reliable and cost-efficient approach. With the help of data received from sensors (typically, time-series data), machine learning can help to create a more efficient process of a maintenance schedule. By reducing unscheduled equipment downtime it can significantly reduce the associated costs. Also, predictive maintenance helps a company keep transparent and trustworthy relations with its customers.
One of the greatest examples of using predictive maintenance is Tesla. Their cars are known as one of the most innovative and reliable, and they keep improving their products. Tesla has recently implemented a new initiative: Tesla cars can now self-diagnose internal problems and order replacement parts from the diagnostic center. A car monitors conditions of all the elements, analyses it, and shows a notification of an issue with the car's power conversion system. The car automatically places an order for replacement parts to be delivered to a Tesla service center, where the owner's car would be repaired.
Implementation of a machine learning approach in predictive maintenance is a complex process that requires a lot of resources. Of course, companies need to collect static and historical data on the operations and monitor their equipment. Once a company has enough data, it's still quite challenging to develop a good ML approach from scratch: it requires time, good expertise, and a lot of pre-processing and feature engineering to determine the failure patterns.
There are two possible ways to implement predictive maintenance using artificial intelligence and machine learning:
Sometimes it's difficult to find a proper tool and not to waste a lot of time. If you're interested in predictive maintenance implementation, you can contact the Datrics team. At Datrics.ai we're working on a platform that helps with analytical routines. Our experienced team of Data scientists will help you with your needs.
With the datrics.ai platform, you can easily analyze big chunks of data and streamline the process of equipment maintenance without any prior technical knowledge.
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