Failure Propagation Prediction of Complex Electromechanical Systems Based on Interdependence
Abstract
:1. Introduction
2. Modeling Method
2.1. Determination of Key Component Set Based on Reliability Measurement
2.1.1. System State Degradation Performance Index
- 1.
- Reliability
- (1)
- Reliability function
- (2)
- Unreliability function
- (3)
- Hazard function
- 2.
- Failure rate
- 3.
- Mean time to failure
2.1.2. Two-Parameter Weibull Function Expression
2.1.3. Parameter Estimation of Weibull Function
2.2. A Calculation Method Considering the Dependence of the Topology Model and Fault Data
2.2.1. Construction of Topological Network Model
- Mechanical connection relationship : the mechanical connection relationship between two physical components such as cementation, welding, riveting, and screw connection.
- Electrical connection relationship : there is a relationship between transmission, conversion, distribution, and utilization of electrical energy between two physical components.
- Information connection relationship : there is information flow transmission or exchange between two physical components through wired or wireless means.
2.2.2. Risk Point Status Parameters Based on Mathematical Characterization Methods
- Mechanical status parameters: refers to the basic design indicators of the component, such as volume, density, temperature, pressure, etc. The pressure value can be directly monitored by the pressure sensor.
- Electric status parameters: refers to the flow value of the circuit system, such as current, voltage, etc., where current and voltage are measured.
- Information status parameters: refers to the pass instructions, data packages, etc., and it can judge the state status by passing the time standards of passing instructions.
2.3. System Failure Chain Construction Method Based on Component Risk State Representation
2.3.1. Non-Linear Dependencies Based on Uncertainty
2.3.2. Component Interdependence Model under Causality
2.3.3. Quantified Inter-Dependencies
2.3.4. Affected Component Sets Based on Machine Learning
3. Case Study on the Braking System of Rail Train
3.1. Construction of Braking System Topology Network Model Based on Reliability Evaluation
3.1.1. Data Sources
3.1.2. Component Reliability Analysis
- (1)
- Reliability analysis of emergency solenoid valve.
- (2)
- Reliability analysis of EP valve.
- (3)
- Reliability analysis of relay valve.
3.1.3. Coupling Topology Network Model of Rail Train Braking System
3.2. State Expression of Key Risk Point State of the Braking System
3.2.1. Emergency Solenoid Valve
3.2.2. EP Valve
3.2.3. Relay Valve
3.2.4. Empty Truck Valve
3.2.5. Brake Cylinder
3.2.6. Wind Source System
3.2.7. The Anti-Skid System
3.3. The Brake System Failure Data Statistics
3.4. The Components under Correlation Depending on the Model
3.4.1. Non-Linear Correlation Coefficient Calculation Based on Uncertainty
3.4.2. Construction of Direct Influence Matrix of the Braking System
3.4.3. Causal Analysis of the Braking System
3.5. Construction of the Brake System Failure Propagation Chain
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Abbreviations and Symbols | Meaning | Calculation Formula |
---|---|---|
Reliability function | ||
Unreliability function | ||
Hazard function | ||
Failure rate | ||
MTTF | Mean Time To Failure | |
MTBF | Mean Time Between Failures | |
Normal working time | ||
Probability of occurrence | ||
Working time | ||
The amount of change in time | ||
Scale parameter | ||
Shape parameter |
Serial Number | Emergency Solenoid Valve | EP Valve | ||||
---|---|---|---|---|---|---|
1 | 600 | 2 | 30 | 800 | 2 | 18 |
2 | 700 | 2 | 28 | 1000 | 2 | 16 |
3 | 800 | 2 | 26 | 1200 | 2 | 14 |
4 | 950 | 3 | 24 | 1500 | 2 | 12 |
5 | 1100 | 3 | 21 | 1750 | 3 | 9 |
6 | 1200 | 3 | 18 | 2000 | 3 | 6 |
7 | 1350 | 3 | 15 | 2150 | 3 | 3 |
8 | 1550 | 4 | 12 | 2300 | 3 | 0 |
9 | 1800 | 4 | 8 | |||
10 | 2000 | 4 | 4 |
Test Count | Lifetime t (Ten Thousand Hours) | Type of Data | Test Count | Lifetime t (Ten Thousand Hours) | Type of Data |
---|---|---|---|---|---|
1 | 0.67 | Fault data | 11 | 2.00 | Censored data |
2 | 1.79 | Fault data | 12 | 2.00 | Censored data |
3 | 1.88 | Fault data | 13 | 2.00 | Censored data |
4 | 1.89 | Fault data | 14 | 2.00 | Censored data |
5 | 1.92 | Fault data | 15 | 2.00 | Censored data |
6 | 2.00 | Censored data | 16 | 2.00 | Censored data |
7 | 2.00 | Censored data | 17 | 2.00 | Censored data |
8 | 2.00 | Censored data | 18 | 2.00 | Censored data |
9 | 2.00 | Censored data | 19 | 2.00 | Censored data |
10 | 2.00 | Censored data | 20 | 2.00 | Censored data |
Faulty Part | Fault Type Description | Fault Statistics | |
---|---|---|---|
Non-Correlated Failure | Correlation Fault | ||
Emergency solenoid valve | BCU failure, emergency braking | 3 | 3 |
EP valve | BCU failure, booster cylinder failure | 2 | 4 |
Relay valve | BCU failure, pressure not relieved | 2 | 4 |
Empty truck valve | BCU failure | 1 | 3 |
Brake cylinder | Insufficient braking force, braking does not ease | 2 | 6 |
Air source system | An air compressor failure, dryer failure | 3 | 16 |
Anti-skid system | Anti-skid valve failure | 1 | 3 |
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Xia, Y.; Yang, N.; Wang, H.; Wang, X.; Cui, M.; Li, M. Failure Propagation Prediction of Complex Electromechanical Systems Based on Interdependence. Electronics 2023, 12, 2600. https://doi.org/10.3390/electronics12122600
Xia Y, Yang N, Wang H, Wang X, Cui M, Li M. Failure Propagation Prediction of Complex Electromechanical Systems Based on Interdependence. Electronics. 2023; 12(12):2600. https://doi.org/10.3390/electronics12122600
Chicago/Turabian StyleXia, Yu, Nan Yang, Hu Wang, Xiaoli Wang, Mengzhen Cui, and Man Li. 2023. "Failure Propagation Prediction of Complex Electromechanical Systems Based on Interdependence" Electronics 12, no. 12: 2600. https://doi.org/10.3390/electronics12122600