Abstract
Diagnosis of bearing faults is crucial in various industries. Time series classification (TSC) assigns each time series to one of a set of pre-defined classes, such as normal and fault, and has been regarded as an appropriate approach for bearing fault diagnosis. Considering late and inaccurate fault diagnosis may have a significant impact on maintenance costs, it is important to classify bearing signals as early and accurately as possible. TSC, however, has a major limitation, which is that a time series cannot be classified until the entire series is collected, implying that a fault cannot be diagnosed using TSC in advance. Therefore, it is important to classify a partially collected time series for early time series classification (ESTC), which is a TSC that considers both accuracy and earliness. Feature-based TSCs can handle this, but the problem is to determine whether a partially collected time series is enough for a decision that is still unsolved. Motivated by this, we propose an indicator of data sufficiency to determine whether a feature-based fault detection classifier can start classifying partially collected signals in order to diagnose bearing faults as early and accurately as possible. The indicator is trained based on the cosine similarity between signals that were collected fully and partially as input to the classifier. In addition, a parameter setting method for efficiently training the indicator is also proposed. The results of experiments using four benchmark datasets verified that the proposed indicator increased both accuracy and earliness compared with the previous time series classification method and general time series classification.
Highlights
Bearings are one of the important components in rotary machines such as motors, wind turbines, helicopters, automobiles, and gearboxes [1]
For dataset #1, the earliness of classifiers with reject option (CWRO) is zero except for the case where the classifier is support vector machine (SVM) and ε is 0.5, implying that there is no clear difference between partially collected time series under normal and fault status, and CWRO is not appropriate for this kind of dataset
Bearing fault detection is one of the most important tasks in the manufacturing industry, which is often accomplished by Time series classification (TSC)
Summary
Bearings are one of the important components in rotary machines such as motors, wind turbines, helicopters, automobiles, and gearboxes [1]. An accelerometer selected several features based on the Mahalanobis distance for training the SVM They observed that a noncontact sensor can be applied to identify bearing faults, and that a linear SVM outperformed other SVMs. Gunerkar et al [16] employed wavelet transform to extract time domain features from vibration signals and trained supervised models, including an artificial neural network (ANN) and a k-nearest neighbor algorithm. The ensemble can reject an instance when it is hard to classify, even though it is fully collected, resulting in lower earliness He et al [20] proposed a shapelet-based early classification method for multivariate time series. This paper proposes a feature-based early classification method for bearing fault diagnosis with a data sufficiency indicator.
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