Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines

This paper proposes a reliable multiple combined fault diagnosis scheme for bearings using heterogeneous feature models and an improved one-against-all multiclass support vector machines (OAA-MCSVM) classifier. Distinct feature extraction methods are simultaneously applied to an acoustic emission (AE) signal to extract unique fault features for diagnosing bearing defects. These fault features are composed of time domain, frequency domain statistical parameters, and complex envelope spectrum analysis. Generally, a high-dimensional feature vector is used to train the standard OAA-MCSVM classifier for diagnosis and identification of bearing defects. However, this classification method ignores individual classifier competence when results from multiple classes are agglomerated for the final decision, and therefore, yields undecided and overlapped feature spaces where classification accuracy is severely degraded. To solve this unreliability problem, this paper introduces a dynamic reliability measure (DReM) technique for individual support vector machines (SVMs) in the one-against-all (OAA) framework. This DReM accounts for the spatial variation of the classifier's performance by finding the local neighborhood of a test sample in the training samples space and defining a new decision function for the OAA-MCSVM. The efficacy of the proposed OAA-MCSVM classifier with DReM is tested for identifying single and multiple combined faults in low-speed bearings. The experimental results demonstrate that the proposed classifier technique is superior to three state-of-the-art algorithms, yielding 6.19–16.59% improvement in the average classification performance.