Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Abstract

Hydraulic systems have the characteristics of strong fault concealment, powerful nonlinear time-varying signals, and a complex vibration transmission mechanism; hence, diagnosis of these systems is a challenge. To provide accurate diagnosis results automatically, numerous studies have been carried out. Among them, signal-based methods are commonly used, which employ signal processing techniques based on the state signal used for extracting features, and further input the features into the classifier for fault recognition. However, their main deficiencies include the following: (1) The features are manually designed and thus may have a lack of objectivity. (2) For signal processing, feature extraction and pattern recognition are conducted using independent models, which cannot be jointly optimized globally. (3) The machine learning algorithms adopted by these methods have a shallow architecture, which limits their capacity to deeply mine the essential features of a fault. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome such deficiencies. Based on deep learning, deep neural networks (DNNs) can automatically learn the complex nonlinear relations implied in a signal, can be globally optimized, and can obtain the high-level features of multi-dimensional data. In this paper, the main technology used in an intelligent fault diagnosis and the current research status of hydraulic system fault diagnosis are summarized and analyzed. The significant prospect of applying deep learning in the field of intelligent fault diagnosis is presented, and the main ideas, methods, and principles of several typical DNNs are described and summarized. The commonality between a fault diagnosis and other issues regarding typical pattern recognition are analyzed, and research ideas for applying DNNs for hydraulic fault diagnosis are proposed. Meanwhile, the research advantages and development trend of DNNs (both domestically and overseas) as applied to an intelligent fault diagnosis are reviewed. Furthermore, the fault characteristics of a complex hydraulic system are summarized and discussed, and the key problems and possible research ideas of applying DNNs to an intelligent hydraulic fault diagnosis are presented and comprehensively analyzed.

Development of a resource-process approach to increasing the efficiency of electrical equipment for food production

“…As a result of rapid technological development, traditio nal systems of automation and standard methods of production (compliance with the technological regulations, reducing intermediate losses through modernization of the production facility, process coordination of the adjacent areas [2]) are not sufficient as the means of receiving high profits. Managers of industrial enterprises are likely to introduce intelligent systems at their production sites [3], which are based on various modern methods and technologies [4][5][6]. The most popular among them are the use of robust [7,8], multidimensional optimal regulators [9], diagnostics [10], neural networks [11], fuzzy logic, scenario-target approach [12,13] and others.…”

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

“…On account of the intensity of operation and varying working conditions, the frequent machinery faults may result in the unexpected severe losses in safety and economy [1,2]. As a typical interdisciplinary cooperation, fault diagnostic techniques can be categorized into the following three categories, modelbased methods, knowledge-based methods, and signal-based methods [3].…”

Fault Diagnosis of Rotating Machinery: A Review and Bibliometric Analysis

“…As a comprehensive review of the basic research on machinery fault diagnosis, Chen et al [3] reviewed the past, summarized the present and analysed future trends, focusing on the four key steps in fault diagnosis: fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. Additional reviews have focused on specific aspects, such as signal processing techniques in fault diagnosis [2,[4][5][6][7][8][9][10][11][12][13][14], fault diagnosis in condition-based maintenance (CBM) and prognostics and health management (PHM) [15], fault diagnosis of bearings, gearboxes and turbines [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31], and AI techniques in fault diagnosis [32][33][34][35]. These reviews have identified various topical issues covered within the emergent research literature and have provided valuable research insight into the corresponding themes.…”