Raw statistical features can imitate the amplitude, average, energy and time, and frequency series distribution of a raw vibration signal. However, these raw statistical features are either not very sensitive to weak incipient faults or are unsuitable for more severe faults, thus affecting the fault detection and classification accuracy. To tackle this problem, this paper proposes a discriminant feature extraction method for Centrifugal Pump (CP) fault diagnosis. In order to obtain the discriminant feature pool, the proposed method is divided into three phases. In the first phase, a healthy baseline signal is selected. In the second phase, the healthy baseline signal is cross-correlated with the CP vibration signals of different classes, and a set of new features are extracted from the resulting correlation sequence. In the third phase, raw hybrid features in time, frequency, and the time-frequency domain are extracted from both the healthy baseline signals and the CP vibration signals of different classes. The correlation coefficient is calculated between the raw hybrid feature pools, which results in a new set of discriminant features. Discriminant features help the machine learning classifiers to effectively detect and classify the data into its respective classes. Furthermore, the proposed method combines all these features into a single feature vector that forms a vulnerable feature pool. The vulnerable feature pool describes the CP's vulnerability to a fault and is provided as an input to a multiclass support vector machine (MSVM) for CP fault detection and classification. The experimental results illustrate that the accuracy obtained from the proposed method shows promising improvements over the state-of-the-art conventional methods.

This paper proposes a Gaussian mixture model-based (GMM) bearing fault band selection (GMM-WBBS) method for signal processing. The proposed method benefits reliable feature extraction using fault frequency oriented Gaussian mixture model (GMM) window series. Selecting exclusively bearing fault frequency harmonics, it eliminates the interference of bearing normal vibrations in the lower frequencies, bearing natural frequencies, and the higher frequency contents that prove to be useful only for anomaly detection but do not provide any insight into the bearing fault location. The features are extracted from time- and frequency- domain signals that exclusively contain the bearing fault frequency harmonics. Classification is done using the Weighted KNN algorithm. The experiments performed with the data containing the vibrations recorded from artificially damaged bearings show the positive effect of utilizing the proposed GMM-WBBS signal processing to filter out the discriminative data of uncertain origin. All comparison methods retrofitted with the proposed method demonstrated classification performance improvements when provided with vibration data with suppressed bearing natural frequencies and higher frequency contents.

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A framework aimed to improve the bearing-fault diagnosis accuracy using a hybrid feature-selection method based on Wrapper-WPT is proposed in this paper. In the first step, the envelope vibration signal of the roller bearing is provided to the Wrapper-WPT. There, it is initially decomposed into several sub-bands using Wavelet Packet Transform (WPT), and a set out of nineteen time and frequency domain features are individually extracted from each sub-band of the decomposed vibration signal forming a wide feature pool. In the following step, Wrapper-WPT constructs a final feature vector using the Boruta algorithm, which selects the most discriminant features from the wide feature pool based on the important metric obtained from the Random Forest classifier. Finally, Subspace k-NN is used to identify the health conditions of the bearing, thus forming a hybrid signal processing and machine learning-based model for bearing fault diagnosis. In comparison with other state-of-the-art methods, the proposed method showed higher classification performance on two different bearing-benchmark vibration datasets with variable operating conditions.

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