With the development of the urban rail train, safety and reliability have become more and more important. In this paper, the fault degree and health degree of the system are put forward based on the analysis of electric motor drive system’s control principle. With the self-organizing neural network’s advantage of competitive learning and unsupervised clustering, the system’s health clustering and safety identification are worked out. With the switch devices’ faults data obtained from the dSPACE simulation platform, the health assessment algorithm is verified. And the results show that the algorithm can achieve the system’s fault diagnosis and health assessment, which has a point in the health assessment and maintenance for the train.
Urban rail train traction drive system is the key subsystem of the train, which is the guarantee for the train’s safe and smooth running. However, urban rail train’s motor drive system is a multivariable, nonlinear, strong coupling complex system. Its failure frequency and failure mode are intricate, mutual coupling interference, which seriously affected the safety and reliability of the train. However, the present process of fault identification and intervention “backwardness” determines its inevitable failure, which is limited for improving the safety. So the traditional urban rail train motor drive system needs online, real-time, fast health assessment and safety assessment. Safety control measures also should be taken in time to ensure the train’s safe running.
Reference [
References [
As can be seen from Figure
AC motor drive system.
Vector control system.
Traction inverter converts the DC voltage required by the traction system to variable voltage and variable frequency three-phase AC power supply for three-phase induction motor. So the inverter’s output voltage and current waveform quality directly affect the performance of motor drive system and also reflect the health status of the motor drive system. In addition, three-phase motor’s output torque and speed also directly reflect the traction motor’s traction ability, which also indirectly reflects the health status of the motor drive system. So the three-phase output voltage, three-phase output current, output torque, and speed are identified as the health variables to analyze the health characteristics of motor drive system.
Lu Murphey et al. [
Suppose the number of sampling voltages
The number of sampled currents
The number of types of sampled torque
The number of sampled speeds
Suppose that the variables’ rating standard values of the system in steady state are
Self-organizing feature map (SOM) model [
SOM neural network.
Self-organizing feature map algorithm is a kind of clustering method without teachers, which can map any input mode into one-, two-, or multidimensional discrete graphics in the output layer and still keep its structure unchanged. The learning process can be described as follows: for each of the networks input health characteristics, it just adjusts parts of the weights, which make the weight vectors more close or far from the input vector. And the adjustment process is called the competitive learning. As the continuous learning, the weight vectors in the input space are separated and form a mode which represents the input space, respectively, which realizes the clustering of the health characteristics and health level.
Figure
The flow chart of network learning and health assessment.
Parameters here are mainly the number of categories. When we use the self-organizing feature map network to cluster, the structure of the competition layer is set as
Among them,
SOM network’s training steps [
The neurons who get the minimum Euclidean distance will win as the winning neuron.
Different distance functions can be used to determine the neighborhood; the commonly used Euclidean distance (dist) is the Manhattan distance (mandist), and so forth.
Update the learning rate
The motor drive fault simulation experiment is conducted by the dSPACE real-time simulation platform [
System health degradation simulation platform based on dSPACE.
Figure
System health degradation state table.
Number | Condition |
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1 | Healthy |
2 |
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3 |
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4 |
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5 |
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6 |
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7 |
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8 |
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9 |
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10 |
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Figure
Top-level block diagram of simulation model.
As shown in Figure
Block diagram of RFOC model.
Figure
Degradation data tables of system health state.
Number | Condition |
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1 | Healthy | 0.05 | 0.03 | 0.02 | 0.03 | 0.04 | 0.02 | 0.01 | 0.02 |
2 |
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0.11 | 0.1 | 0.13 | 0.09 | 0.16 | 0.08 | 0.15 | 0.1 |
3 |
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0.23 | 0.2 | 0.19 | 0.16 | 0.18 | 0.24 | 0.22 | 0.17 |
4 |
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0.26 | 0.33 | 0.28 | 0.32 | 0.31 | 0.34 | 0.33 | 0.35 |
5 |
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0.41 | 0.45 | 0.38 | 0.42 | 0.46 | 0.47 | 0.42 | 0.4 |
6 |
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0.71 | 0.75 | 0.68 | 0.72 | 0.66 | 0.57 | 0.72 | 0.74 |
7 |
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0.81 | 0.85 | 0.78 | 0.82 | 0.86 | 0.77 | 0.82 | 0.84 |
Motor drive system fault simulation test platform.
Different switching devices faults are triggered to simulate different fault degrees of motor drive system. The health degraded data in Table
System health assessment results.
Number | Condition | Fault degree | Health degree |
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1 | Healthy | 0.1 | 90% |
2 |
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0.2 | 80% |
4 |
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0.4 | 60% |
6 |
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0.5 | 50% |
8 |
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0.6 | 40% |
9 |
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0.7 | 30% |
10 |
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0.9 | 10% |
SOM neighbor connections.
SOM neighbor weight distances.
SOM weight positions.
This paper extracted the health variables of the motor drive system by analyzing the control principles and fault mechanism firstly. Then they are preprocessed to get the health degree. With the self-organizing feature map network’s unsupervised and autonomous learning characteristics, the system fault is clustered and recognized quickly through the competition clustering. The fault of the switching device is taken as example to validate the algorithm by the simulation experiment and demonstration. Finally the health degree is put forward to complete the system’s health assessment, which has an important guiding significance for railway motor drive system’s safety assessment and maintenance.
Of course, due to the limited time and ability, this paper just put forward a preliminary health assessment scheme and algorithm. Later there is a need for research of the capacitance’s aging damage, electrical insulation failure, sensor failure, and also the analysis of different failure mode effect on the system in order to realize the online health assessment and safety early warning for the train’s safety, reliability, and stability.
The authors declare that they have no competing interests.
The work was supported by the National Natural Science Foundation of High Speed Rail Joint Funds (U1134204).