Failure prediction for multiple connected devices

画像: 複数のデバイスに跨る故障予測(英語)

Failure prediction is an important problem in industry and has been studied over decades in various areas. Generally, majority of equipment and industrial components deteriorate after running for a period of time. The failures among multiple devices, which are physically connected to each other, may propagate. When a component of a system fails, other relevant components may break down too. For example, in a mill plant, when a motor fails, the bearings, which are physically connected to it, may fail as well. To capture these relationships, model-based techniques use system equations to extract analytical redundancies between devices’ measurements [1, 2]. An alternative approach is to apply a data-driven solution that uses information from similar devices for failure prediction. Data-driven methods use system’s measurements as the features for failure prediction [3]. In order to predict multivariate responses from multiple devices simultaneously with higher accuracy, we use the correlated features from these devices. We introduced multi-Bernoulli distribution with logit transformation to learn the correlation between the predictors and multivariate responses.


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