Incipient winding fault detection and isolation for induction motors of high-speed trains

2017 ◽  
Author(s):  
Yunkai Wu ◽  
Bin Jiang ◽  
Ningyun Lu
Keyword(s):  
Fault Detection ◽  
High Speed ◽  
Induction Motors ◽  
Fault Detection And Isolation ◽  
High Speed Trains

Image-Based Versus Signal-Based Sensors for Machine Fault Detection and Isolation

2014 ◽  
Author(s):  
Heshan Fernando ◽  
Vedang Chauhan ◽  
Brian Surgenor
Keyword(s):  
Fault Detection ◽  
High Speed ◽  
Fault Isolation ◽  
Video Data ◽  
Fault Detection And Isolation ◽  
Single Camera ◽  
Multiple Faults ◽  
Multiple Sensors ◽  
Machine Fault ◽  
And Performance

This paper presents the results of a comparative study that investigated the use of image-based and signal-based sensors for fault detection and fault isolation of visually-cued faults on an automated assembly machine. The machine assembles 8 mm circular parts, from a bulk-supply, onto continuously moving carriers at a rate of over 100 assemblies per minute. Common faults on the machine include part jams and ejected parts that occur at different locations on the machine. Two sensor systems are installed on the machine for detecting and isolating these faults: an image-based system consisting of a single camera and a signal-based sensor system consisting of multiple greyscale sensors and limit switches. The requirements and performance of both systems are compared for detecting six faults on the assembly machine. It is found that both methods are able to effectively detect the faults but they differ greatly in terms of cost, ease of implementation, detection time and fault isolation capability. The conventional signal-based sensors are low in cost, simple to implement and require little computing power, but the installation is intrusive to the machine and readings from multiple sensors are required for faster fault detection and isolation. The more sophisticated image-based system requires an expensive, high-resolution, high-speed camera and significantly more processing power to detect the same faults; however, the system is not intrusive to the machine, fault isolation becomes a simpler problem with video data, and the single camera is able to detect multiple faults in its field of view.

scholarly journals H∞ mixed sensitivity optimization for high speed tilting trains

2020 ◽  
Vol 9 (5) ◽  
pp. 1854-1860
Author(s):  
Fazilah Hassan ◽  
Argyrios Zolotas ◽  
Shaharil Mohd Shah
Keyword(s):  
Signal Processing ◽  
Fault Detection ◽  
Disturbance Rejection ◽  
High Speed ◽  
Important Research ◽  
Robust Controller ◽  
Trade Off ◽  
High Speed Trains ◽  
Simple Nature ◽  
Research Studies

The industrial norm of tilting high speed trains, nowadays, is that of Precedence tilt (also known as Preview tilt). Precedence tilt, although succesfull as a concept, tends to be complex (mainly due to the signal interconnections between vehicles and the advanced signal processing required for monitoring). Research studies of early prior to that of precedence tilt schemes, i.e. the so-called Nulling-type schemes, utilized local-per-vehicle signals to provide tilt action (this was essentially a typical disturbance rejection-scheme) but suffered from inherent delays in the control). Nulling tilt may still be seen as an important research aim due to the simple nature and most importantly due to the more straightforward fault detection compared to precedence schemes. The work in this paper presents a substantial extension conventional to robust H∞ mixed sensitivity nulling tilt control in literature. A particular aspect is the use of optimization is used in the design of the robust controller accompanied by rigorous investigation of the conflicting deterministic/stochastic local tilt trade-off 

scholarly journals A fault detection method for a braking system of high-speed trains

2020 ◽  
Vol 50 (4) ◽  
pp. 483-495
Author(s):  
Tianxu GUO ◽  
Junfeng ZHANG ◽  
Maoyin CHEN ◽  
Jianxue SANG ◽  
Donghua ZHOU ◽  
...  
Keyword(s):  
Fault Detection ◽  
High Speed ◽  
Detection Method ◽  
Braking System ◽  
High Speed Trains

scholarly journals Sensors Information Fusion System with Fault Detection Based on Multi-Manifold Regularization Neighborhood Preserving Embedding

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1440
Author(s):  
Jianping Wu ◽  
Bin Jiang ◽  
Hongtian Chen ◽  
Jianwei Liu
Keyword(s):  
Fault Detection ◽  
Information Fusion ◽  
High Speed ◽  
Euclidean Distance ◽  
Manifold Regularization ◽  
Fusion System ◽  
Electrical Drive ◽  
High Speed Trains ◽  
Sensor Information ◽  
Drive Systems

Electrical drive systems play an increasingly important role in high-speed trains. The whole system is equipped with sensors that support complicated information fusion, which means the performance around this system ought to be monitored especially during incipient changes. In such situation, it is crucial to distinguish faulty state from observed normal state because of the dire consequences closed-loop faults might bring. In this research, an optimal neighborhood preserving embedding (NPE) method called multi-manifold regularization NPE (MMRNPE) is proposed to detect various faults in an electrical drive sensor information fusion system. By taking locality preserving embedding into account, the proposed methodology extends the united application of Euclidean distance of both designated points and paired points, which guarantees the access to both local and global sensor information. Meanwhile, this structure fuses several manifolds to extract their own features. In addition, parameters are allocated in diverse manifolds to seek an optimal combination of manifolds while entropy of information with parameters is also selected to avoid the overweight of single manifold. Moreover, an experimental test based on the platform was built to validate the MMRNPE approach and demonstrate the effectiveness of the fault detection. Results and observations show that the proposed MMRNPE offers a better fault detection representation in comparison with NPE.

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