![]() ![]() At the end of the majority of the series, we can observe a divergent behavior, which announces a future failure. To plot is always a good idea… In this way, we can have an impressive and general overview of the data at our disposal. To understand better this explanation we try to have a look at the data: train_df.id.value_counts().plot.bar() The objective is to predict the number of remaining operational cycles before failure in the test set, i.e., the number of operational cycles after the last cycle that the engine will continue to operate. In the test set, the time series ends some time prior to system failure. In the training set, the fault grows in magnitude until system failure. The engine is operating normally at the start of each time series, and develops a fault at some point during the series. observations in terms of time for working life. Data are available in the form of time series: 3 operational settings, 21 sensor measurements and cycle - i.e. Turbofan Engine Degradation Simulation Dataset, provided by NASA, is becoming an important benchmark in the Remaining Useful Life ( RUL) estimation for a fleet of engines of the same type (100 in total). So the first step to achieving good performance is to try to have at disposal the richest dataset that treats every kind of possible scenario. THE DATASETįor Data Scientists the most important problem, when dealing with this kind of task, is the lack of rare events in the form of observations available. To achieve this target I developed a Convolutional NN in Keras that deals with time series in the form of images. This kind of problem plays a key role in the field of Predictive Maintenance, where the purpose is to say ‘ How much time is left before the next fault?’. ![]() In this post, I’ve developed a Machine Learning solution to predict the Remaining Useful Life ( RUL) of a particular engine component. Given these scenarios, we can imagine a rare event as a particular state that occurs under specific conditions, divergent from normal behaviors, but which plays a key role in terms of economic interest. Survival Analysis, Customer churn prediction, Predictive Maintenance and Anomaly Detection are some examples of the most popular fields of application that deal with rare events. Predict rare events is becoming an important topic of research and development in a lot of Artificial Intelligent solutions. ![]()
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