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.

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Discriminant Feature Extraction for Centrifugal Pump Fault Diagnosis

This paper proposes a three-stage fault diagnosis strategy for multistage centrifugal pumps. First, the proposed method identifies and selects fault characteristic modes of vibration to overcome the substantial noise produced by other unrelated macro-structural vibrations. In the second stage, raw hybrid statistical features are extracted from the fault characteristic modes of vibration in time, frequency, and the time-frequency domain. These extracted features result in a high-dimensional feature space. However, in general, not all of the features are best to characterize the ongoing processes in a centrifugal pump, and some of the extracted features might be irrelevant or even redundant, which can affect the fault classification capabilities of the classification algorithm. In the third stage, a novel dimensionality reduction technique, called Pearson Linear Discriminant Analysis (PLDA), is introduced. PLDA assesses the helpfulness of the feature parameters. This technique selects highly interclass-correlated features and adds them to a helpful feature pool. To achieve maximum intraclass separation while maintaining the original class information, linear discriminant analysis is then applied to the helpful feature pool. This combination of helpful feature pool formation and linear discriminant analysis forms the proposed application of PLDA. The reduced discriminant feature set obtained from PLDA is then classified using the k-nearest neighbor classification algorithm. The proposed method outperforms the previously presented methods in terms of classification accuracy.

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Multistage Centrifugal Pump Fault Diagnosis by Selecting Fault Characteristic Modes of Vibration and Using Pearson Linear Discriminant Analysis

Centrifugal pumps are important types of electro-mechanical machines used for fluid and energy conveyance. Mechanical faults in centrifugal pumps lead to abnormal impacts in the vibration signal of the system. Those impacts induce nonstationarity in vibration signals and hence complex time-frequency domain signal analysis techniques are required to investigate the mechanical fault features of centrifugal pumps. In this paper, an end-to-end pipeline for diagnosing faults in centrifugal pumps is proposed. To create a two-dimensional representation of the transients that appear in the vibration signals due to centrifugal pump operating conditions, first, a 1/3-binary tree fast kurtogram is computed. Next, a convolutional autoencoder and convolutional neural network are trained to autonomously extract global and local features from the kurtograms. Then, global, and local features are merged to form a joined feature vector that contains different visual features that are extracted using convolutional deep architectures using their specific loss functions during the training. Finally, this feature vector is propagated to a shallow-structured artificial neural network to accomplish fault identification. The proposed framework has been validated by the dataset collected from a real industrial centrifugal pump test rig. The results obtained during the series of experimental trials demonstrated that the introduced method achieved high classification accuracies when diagnosing faults based on signals collected under 3.0 and 4.0 bars of pressure.

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Global and Local Feature Extraction Using a Convolutional Autoencoder and Neural Networks for Diagnosing Centrifugal Pump Mechanical Faults

Predictive maintenance based on condition monitoring uses sensor and system data to prevent damage, in advance to a failure occurring, allowing for a service to be performed at an optimal position ...

Vibration analysis for predictive maintenance of a rotary pump : Optimal accelerometer configuration based on vibration analysis for cavitation detection of a bi-winged positive displacement pump

Siddharth M. Chittora

Papers

Monitoring of Mechanical Seals in Process Pumps