Review on model-based methods for on-board condition monitoring in railway vehicle dynamics

Abstract

This article performs an extensive review on condition monitoring techniques for rail vehicle dynamics. In particular, the review focuses on applications of model-based approaches for on-board condition monitoring systems. The article covers condition monitoring schemes, fault detection strategies as well as theoretical aspects of different techniques. Case studies and experimental applications are also summarized. All the mentioned issues are discussed with the goal of providing a detailed overview on condition monitoring in railway vehicle dynamics.

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

“…The term “condition monitoring” evolved—in the case of research connected to rail transport—with the development of instruments that allow the development of increasingly better measurement analyses. Such an evolution of terms, definitions, and approaches connected to condition monitoring in the case of the aforementioned mode of transport is presented in Table 1 , based on [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ].…”

“…This is an insignificant number considering the essence of the subject matter; nevertheless, it is worth underlining that this number concerns some limited areas that will be defined later in the current article. By recalling the published review papers, it can be stated that their authors have taken under consideration topics such as (certainly, the below mentioned topics were considered in research papers as well; however, since review papers are treated as certain summaries, research papers are omitted in the following list): condition monitoring techniques for rail vehicle dynamics [ 10 ], applications and various soft computing methods used for condition monitoring in the context of intelligent systems [ 46 ], description of onboard condition monitoring systems, methods, and devices—with a particular interest in microelectromechanical sensors, microprocessors, and transceivers—creating wireless sensor networks for freight rail transport [ 47 ], methods, techniques, and applications connected to the condition monitoring of railroad switches and crossing systems [ 48 ], condition monitoring systems and enabling automatic railroad track inspection [ 49 ], railroad track condition monitoring with the use of inertial sensors (inertial measurement sensors) and GPS signals [ 50 ], brief discussion on machine learning techniques in relation to rail track condition monitoring [ 51 ], railway turnout condition monitoring [ 52 , 53 ]), various aspects connected to the industrial Internet of Things solutions related to condition monitoring [ 54 ], various types of sensors in wired and wireless sensor networks [ 23 ], and wireless-sensor-network-based condition monitoring applied in the case of the rail industry [ 32 , 55 ], condition monitoring associated with fault diagnosis of rolling bearings [ 56 ]. …”

Modern Condition Monitoring Systems for Railway Wheel-Set Dynamics: Performance Analysis and Limitations of Existing Techniques

“…The residual evaluation scheme is shown in Figure 4. In this section, the model-based estimation techniques will be reviewed which present on-board monitoring methods applied for the estimation of dynamics of wheel-set [32]. Model-based estimation schemes for wheel-set dynamics are divided into two groups in the account of algorithms i.e., Kalman filter and its extension forms and other modelbased algorithms.…”

Systems thinking approach for improving maintenance management of discrete rail assets: a review and future perspectives

“…Model-based approaches map the mathematical relationships between the input and output signals of dynamic systems, cf. OBrien, Quirke, Bowe, and Cantero (2018), Odashima, Azami, Naganuma, Mori, and Tsunashima (2017) Strano and Terzo (2019), while signal-based approaches use signal processing, statistical analysis, and recently machine learning techniques on the system response signals to draw conclusions on the input data, cf. de Rosa et al (2020), Salvador et al (2016) and Wei, Liu, and Jia (2016).…”