{"id":2148,"date":"2021-04-20T09:44:10","date_gmt":"2021-04-20T08:44:10","guid":{"rendered":"http:\/\/syram.eu\/?p=2148"},"modified":"2021-04-20T10:26:05","modified_gmt":"2021-04-20T09:26:05","slug":"apprentissage-automatique-pour-la-maintenance-predictive-par-ou-commencer","status":"publish","type":"post","link":"https:\/\/syram.eu\/en\/machine-learning-for-predictive-maintenance-by-or-start\/","title":{"rendered":"Machine learning for predictive maintenance: where to start?"},"content":{"rendered":"
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
<\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t

Machine learning for predictive maintenance: where to start?\n<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\"predictive\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

What is predictive maintenance? Think of all the machines you use in a year, everything from a toaster every morning to a plane every summer vacation. Now imagine that, from now on, one of them would break down every day. What impact would that have? <\/span>
<\/p>\n

We are surrounded by machines that make our lives easier, but we are also increasingly dependent on them. Therefore, the quality of a machine depends not only on its usefulness and efficiency, but also on its reliability. And with reliability comes maintenance.<\/span><\/p>\n

When the impact of a failure cannot be tolerated, such as a faulty aircraft engine for example, the machine is subjected to preventive maintenance, which involves periodic inspection and repair; often scheduled according to the time of service. <\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\tThus, the challenge of proper planning increases with the complexity of the machines: in a system with many components working together and influencing each other's lifetime; how can we find the right time when maintenance should be performed so that components are not replaced prematurely but the whole system continues to operate reliably?\u00a0<\/span>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

So, the objective of predictive maintenance is to answer this question. Thus, we seek to build models that quantify the risk of machine failure at any point in time and use this information to improve maintenance planning.<\/span><\/p>

The success of predictive maintenance models depends on three main elements: having the right data, defining the problem appropriately and evaluating the predictions correctly.<\/b><\/p>

In this article, we will expand on the first two points and provide information on how to choose the modeling technique that best fits the question you are trying to answer and the data you have.<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t

DATA COLLECTION\n<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

First, to create a failure model, we need sufficient historical data to capture information about the events leading to a failure.\u00a0<\/span><\/p>

In addition to this, valuable information comes from the general \"static\" characteristics of the system; such as mechanical properties, average usage and operating conditions. However, more data is not always better. When collecting data and supporting a failure model, it is important to take an inventory of the following:<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\tWhat types of failures can occur? Which ones will we try to predict?
\n\nWhat does the \"failure process\" look like? Is it a slow or acute degradation process?
\n\nWhat parts of the machine\/system could be related to each type of failure? What can be measured on each that reflects their condition? How often and how accurately should these measurements be made?<\/span>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

For example, the life of the machines is usually in the order of a few years; this means that data must be collected for an extended period of time in order to observe the system throughout its degradation process.<\/span><\/p>

In an ideal scenario, the data scientist would be involved in the data collection plan to ensure that the data collected is suitable for the model to be built. However, what most often happens in real life is that the data has already been collected before the data scientist arrives and he\/she has to try to make the most of what is available.<\/span><\/p>

So, depending on the characteristics of the system and the available data, a good framing of the model to be built is essential: what question do we want the model to answer and is it possible with the data we have?<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t

THE DEFINITION OF THE PROBLEM\n<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

When thinking about how to define a predictive maintenance model, it is important to keep a few questions in mind:<\/span><\/p>