For ensuring the safety and reliability of high-speed trains, fault diagnosis (FD) technique plays an important role. Benefiting from the rapid developments of artificial intelligence, intelligent FD (IFD) strategies have obtained much attention in the field of academics and applications, where the qualitative approach is an important branch. Therefore, this survey will present a comprehensive review of these qualitative approaches from both theoretical and practical aspects. The primary task of this paper is to review the current development of these qualitative IFD techniques and then to present some of the latest results. Another major focus of our research is to introduce the background of high-speed trains, like the composition of the core subsystems, system structure, etc., based on which it becomes convenient for researchers to extract the diagnostic knowledge of high-speed trains, where the purpose is to understand how to use these types of knowledge. By reasonable utilization of the knowledge, it is hopeful to address various challenges caused by the coupling among subsystems of high-speed trains. Furthermore, future research trends for qualitative IFD approaches are also presented.

A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches

Broad learning system (BLS) is widely used in intelligent fault diagnosis (IFD) since its high computation efficiency and incremental learning ability. However, its applicability is limited to the single-task learning scenario in which only one diagnosis task needs to be learned. Generally, a machinery system contains multiple critical components that need to be diagnosed. The fault data of different components will be collected at different times for model training. It is essentially a task-incremental learning scenario in which the diagnosis model needs to learn a series of diagnosis task for different components at different times. Existing BLS methods cannot meet this requirement due to the catastrophic forgetting issue. Therefore, this paper proposes a task-incremental broad learning system (TiBLS) for multi-component IFD. The TiBLS is developed as a multi-head configuration with a series of BLS blocks to learn different diagnosis tasks sequentially. Then, the catastrophic forgetting is prevented via parameter isolation. Finally, the structure-incremental learning ability is developed for the TiBLS to enhance the diagnosis performance of each task without retraining. In this way, the TiBLS will gain more and more functions to diagnose different components over time. The experiment validation is implemented on a simulated machinery system including three critical components. The diagnosis accuracies of the three tasks are 94.75%, 93.02%, and 92.00%, respectively. The training times of the three tasks are 22.2 s, 34.5 s, and 7.6 s, respectively. The satisfying results demonstrate that the TiBLS is an effective and efficient method for multi-component IFD. 

Task-incremental broad learning system for multi-component intelligent fault diagnosis of machinery

The Dempster–Shafer evidence theory has been widely used in the field of data fusion. However, with further research, incomplete information under the open world assumption has been discovered as a new type of uncertain information. The classical Dempster’s combination rules are difficult to solve the related problems of incomplete information under the open world assumption. At the same time, partial information entropy, such as the Deng entropy is also not applicable to deal with problems under the open world assumption. Therefore, this paper proposes a new method framework to process uncertain information and fuse incomplete data. This method is based on an extension to the Deng entropy in the open world assumption, negation of basic probability assignment (BPA), and the generalized combination rule. The proposed method can solve the problem of incomplete information under the open world assumption, and obtain more uncertain information through the negative processing of BPA, which improves the accuracy of the results. The results of applying this method to fault diagnosis of electronic rotor examples show that, compared with the other uncertain information processing and fusion methods, the proposed method has wider adaptability and higher accuracy, and is more conducive to practical engineering applications.

https://www.mdpi.com/2227-7390/9/11/1292/pdf

An Improved Approach of Incomplete Information Fusion and Its Application in Sensor Data-Based Fault Diagnosis

Feature extraction plays a crucial role in the diagnosis of rotating machinery faults. However, the vibration signals measured are inherently complex and non-stationary and the features of faulty signals are often submerged by noise. The principle and method of blind source separation are introduced, and we point out that the blind source separation algorithm is invalid in an environment of strong impulse noise. In order to solve the problem of fast separation of multi-sensor signals in an environment of strong impulse noise, first, the window width of the median filter (MF) is calculated according to the sampling frequency, so that the impulse noise and part of the white noise can be effectively filtered out. Next, the filtered signals are separated by the improved second-order blind identification (SOBI) algorithm. At the same time, the method is tested on the strong pulse background noise and rub impact dataset. The results show that this method has higher efficiency and accuracy than the direct separation method. It is possible to apply the method to real-time signal analysis due to its speed and efficiency.

Fault Diagnosis of Rotating Machinery Based on Multi-Sensor Signals and Median Filter Second-Order Blind Identification (MF-SOBI)

Timber damage identification using dynamic broad network and ultrasonic signals

Faults happen inevitably in traction systems and thus place the security of the whole high-speed train at risk. In order to improve the safety and reliability of high-speed trains, this article deals with fault detection and diagnosis (FDD) problem for traction systems. Because of high sampling frequency of equipped sensors, FDD strategies in the supervision system of high-speed trains should be of enough high computation efficiency, which is a great bottleneck for artificial intelligence-based FDD methods. For reducing the computational load while maintaining the satisfactory diagnostic accuracy, an enhanced FDD architecture using the modified principal component analysis and broad learning system is developed in this article. Based on the proposed data-driven design whose core is to extract fault information, fast and accurate FDD can be achieved without requirements for mathematical models or control mechanism of high-speed trains. The effectiveness and feasibility of the proposed online design are illustrated on the traction control platform of high-speed trains.

Enhanced Fault Diagnosis Using Broad Learning for Traction Systems in High-Speed Trains

As one of the critical components of high-speed trains, the running gears system directly affects the operation performance of the train. This paper proposes a state-degradation-oriented method for fault diagnosis of an actual running gears system based on the Wiener state degradation process and multi-sensor filtering. First of all, for the given measurements of the high-speed train, this paper considers the information acquisition and transfer characteristics of composite sensors, which establish a distributed topology for axle box bearing. Secondly, a distributed filtering is built based on the bilinear system model, and the gain parameters of the filter are designed to minimize the mean square error. For a better presentation of the degradation characteristics in actual operation, this paper constructs an improved nonlinear model. Finally, threshold is determined based on the Chebyshev’s inequality for a reliable fault diagnosis. Open datasets of rotating machinery bearings and the real measurements are utilized in the case studies to demonstrate the effectiveness of the proposed method. Results obtained in this paper are consistent with the actual situation, which validate the proposed methods.

State-Degradation-Oriented Fault Diagnosis for High-Speed Train Running Gears System.

In this paper, for a class of state estimation models 0204-nf-003883 with degradation process and mean reversion process, the optimal state estimation problem of measured data in partial random walk process is studied. In actual large mechanical systems, data collected by sensors will generate partial random walk characteristics according to system changes, which can be divided into two parts: long-range correlation characteristics and mean reversion process characteristics. In the traditional state estimation model, the influence of the correlation between the data on the state estimation is not considered, so the fitting degree of the actual state is not great. This paper presents an improved state estimation model based on measured data, through the Hurst index to identify the characteristics of the data, and according to the corresponding feature to modify estimation model. At then, by a group of NASA’s lithium ion battery public data sets and a set of numerical simulation, respectively on two groups of properties are verified, prove the effectiveness of the proposed algorithm.

Weijun Wang

Papers

Enhanced Fault Diagnosis Using Broad Learning for Traction Systems in High-Speed Trains

State-Degradation-Oriented Fault Diagnosis for High-Speed Train Running Gears System.

State Estimation with Partial Random Walk