An Intelligent Framework for Fault Diagnosis of Centrifugal Pump Leveraging Wavelet Coherence Analysis and Deep Learning

Abstract

This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps (CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault-related impulses in the CP vibration signal are often attenuated due to the background interference noises, thus affecting the sensitivity of the traditional statistical features towards faults. Furthermore, extracting health-sensitive information from the vibration signal needs human expertise and background knowledge. To extract CP health-sensitive features autonomously from the vibration signals, the proposed approach initially selects a healthy baseline signal. The wavelet coherence analysis is then computed between the healthy baseline signal and the signal obtained from a CP under different operating conditions, yielding coherograms. WCA is a signal processing technique that is used to measure the degree of linear correlation between two signals as a function of frequency. The coherograms carry information about the CP vulnerability towards the faults as the color intensity in the coherograms changes according to the change in CP health conditions. To utilize the changes in the coherograms due to the health conditions of the CP, they are provided to a Convolution Neural Network (CNN) and a Convolution Autoencoder (CAE) for the extraction of discriminant CP health-sensitive information autonomously. The CAE extracts global variations from the coherograms, and the CNN extracts local variations related to CP health. This information is combined into a single latent space vector. To identify the health conditions of the CP, the latent space vector is classified using an Artificial Neural Network (ANN). The proposed method identifies faults in the CP with higher accuracy as compared to already existing methods when it is tested on the vibration signals acquired from real-world industrial CPs.

Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method

“…In order to obtain the node feature matrix X, the features are first captured from the input data using a 1-D CNN. The mapping for extracting features can be represented as follows: (10) where f represents the extracted features, F represents the 1-D CNN, and x input represents the input data. Zhao et al [24] proposed a graph generation layer (GGL) to obtain the adjacency matrix A.…”

“…In order to automatically extract fault features and implement high-efficiency diagnosis, deep learning (DL)-based diagnosis methods have become research hotspots in recent years [7,8]. Compared with traditional methods based on signal analysis and machine learning, DL-based diagnosis methods can reduce human experience interference and have more advantages in the field of intelligent diagnosis [9,10]. In addition, deep learning has further evolved to encompass transfer learning, federated learning, meta-learning, and other advanced paradigms [11].…”

Intelligent Mechanical Fault Diagnosis Method for High-Voltage Circuit Breakers Based on Grey Wolf Optimization and Multi-Grained Cascade Forest Algorithms

“…Ullah et al utilized wavelet coherence analysis and deep learning to accurately identify faults in centrifugal pumps [7]. Siddique et al employed deep learning in combination with enhanced short-time Fourier transform spectrograms and continuous wavelet transform scalograms to precisely detect and classify pipeline leaks [8].…”

Fault Diagnosis of a Multistage Centrifugal Pump Using Explanatory Ratio Linear Discriminant Analysis

“…Wavelet coherence analysis is a state-of-the-art method used for FD in MCPs. It generates coherograms and then applies deep-learning techniques [ 31 ]. This paper diverges from previous studies that concentrated on a specific frequency range, and this study examines vibration modes across the entire spectrum of a defective MCP.…”