Railway point machine prognostics based on feature fusion and health state assessment

This paper presents a condition monitoring approach for point machine prognostics to increase the reliability, availability, and safety in railway transportation industry. The proposed approach is composed of three steps: 1) health indicator (HI) construction by data fusion, 2) health state assessment, and 3) failure prognostics. In Step 1, the time-domain features are extracted and evaluated by hybrid and consistency feature evaluation metrics to select the best class of prognostics features. Then, the selected feature class is combined with the adaptive feature fusion algorithm to build a generic point machine HI. In Step 2, health state division is accomplished by time-series segmentation algorithm using the fused HI. Then, fault detection is performed by using a support vector machine classifier. Once the faulty state has been classified (i.e., incipient/starting fault), the single spectral analysis recurrent forecasting is triggered to estimate the component remaining useful life. The proposed methodology is validated on in-field point machine sliding-chair degradation data. The results show that the approach can be effectively used in railway point machine monitoring

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Feasibility of Applying Mel-Frequency Cepstral Coefficients in a Drive-by Damage Detection Methodology for High-Speed Railway Bridges

In this paper, a drive-by damage detection methodology for high-speed railway (HSR) bridges is addressed, to appraise the application of Mel-frequency cepstral coefficients (MFCC) to extract the Damage Index (DI). A finite element (FEM) 2D VTBI model that incorporates the train, ballasted track and bridge behavior is presented. The formulation includes track irregularities and a damaged condition induced in a specified structure region. The feasibility of applying cepstrum analysis components to the indirect damage detection in HSR by on-board sensors is evaluated by numerical simulations, in which dynamic analyses are performed through a code implemented in MATLAB. Different damage scenarios are simulated, as well as external excitations such as measurement noises and different levels of track irregularities. The results show that MFCC-based DI are highly sensitive regarding damage detection, and robust to the noise. Bridge stiffness can be recognized satisfactorily at high speeds and under different levels of track irregularities. Moreover, the magnitude of DI extracted from MFCC is related to the relative severity of the damage. The results presented in this study should be seen as a first attempt to link cepstrum-based features in an HSR drive-by damage detection approach.info:eu-repo/semantics/publishedVersio

Fault detection and diagnosis methods for engineering systems

The main aim of this thesis is to investigate available techniques and develop a methodology for the fault detection and diagnostics for two engineering systems, namely railway point systems (RPS) and three-phase separators (TPS). The fault detection of the RPS was performed on the measured current from the motor of point operating equipment (POE). The method of One Class Support Vector Machines has been chosen as the fault detection model. Elastic similarity measures, such as edit distance with real penalties and dynamic time warping, were chosen to compare the data of POE operations. A combination of Euclidean distance and elastic similarity measures has been proposed in order to take into account the absolute values and shape properties of the two compared time series. The proposed methodology has been tested on the in-field RPS data. The results indicated that the fault detection model was able to detect abnormal values and/or shape of the time series of measured current. However, not in all cases these changes could be related to a recorded failure of RPS in the database. The fault detection of TPS was performed given the sensor readings of flow and level transmitters of TPS. The method of Bayesian Belief Networks (BBN) has been proposed to overcome the late detection of faults with the threshold based alarm technique. An approach to observe sensor readings of TPS in several adjacent time intervals and to update the prior probabilities in the BBN after inserting the sensor readings as evidence was proposed. The proposed methodology has been tested on the data obtained from a TPS simulation model. The results indicated that the fault detection and diagnostics model was able to detect inconsistencies in sensor readings and link them to corresponding failure modes when single or multiple failures were present in the TPS

Fault detection and diagnosis methods for engineering systems

The main aim of this thesis is to investigate available techniques and develop a methodology for the fault detection and diagnostics for two engineering systems, namely railway point systems (RPS) and three-phase separators (TPS). The fault detection of the RPS was performed on the measured current from the motor of point operating equipment (POE). The method of One Class Support Vector Machines has been chosen as the fault detection model. Elastic similarity measures, such as edit distance with real penalties and dynamic time warping, were chosen to compare the data of POE operations. A combination of Euclidean distance and elastic similarity measures has been proposed in order to take into account the absolute values and shape properties of the two compared time series. The proposed methodology has been tested on the in-field RPS data. The results indicated that the fault detection model was able to detect abnormal values and/or shape of the time series of measured current. However, not in all cases these changes could be related to a recorded failure of RPS in the database. The fault detection of TPS was performed given the sensor readings of flow and level transmitters of TPS. The method of Bayesian Belief Networks (BBN) has been proposed to overcome the late detection of faults with the threshold based alarm technique. An approach to observe sensor readings of TPS in several adjacent time intervals and to update the prior probabilities in the BBN after inserting the sensor readings as evidence was proposed. The proposed methodology has been tested on the data obtained from a TPS simulation model. The results indicated that the fault detection and diagnostics model was able to detect inconsistencies in sensor readings and link them to corresponding failure modes when single or multiple failures were present in the TPS

Radio frequency communication and fault detection for railway signalling

The continuous and swift progression of both wireless and wired communication technologies in today's world owes its success to the foundational systems established earlier. These systems serve as the building blocks that enable the enhancement of services to cater to evolving requirements. Studying the vulnerabilities of previously designed systems and their current usage leads to the development of new communication technologies replacing the old ones such as GSM-R in the railway field. The current industrial research has a specific focus on finding an appropriate telecommunication solution for railway communications that will replace the GSM-R standard which will be switched off in the next years. Various standardization organizations are currently exploring and designing a radiofrequency technology based standard solution to serve railway communications in the form of FRMCS (Future Railway Mobile Communication System) to substitute the current GSM-R. Bearing on this topic, the primary strategic objective of the research is to assess the feasibility to leverage on the current public network technologies such as LTE to cater to mission and safety critical communication for low density lines. The research aims to identify the constraints, define a service level agreement with telecom operators, and establish the necessary implementations to make the system as reliable as possible over an open and public network, while considering safety and cybersecurity aspects. The LTE infrastructure would be utilized to transmit the vital data for the communication of a railway system and to gather and transmit all the field measurements to the control room for maintenance purposes. Given the significance of maintenance activities in the railway sector, the ongoing research includes the implementation of a machine learning algorithm to detect railway equipment faults, reducing time and human analysis errors due to the large volume of measurements from the field

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance