# Hybrid Wavelet–CNN Fault Diagnosis Method for Ships’ Power Systems

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Related Works

## 3. System Description

#### 3.1. Description of Simulation Model

#### 3.2. Running the Simulation

## 4. Research Methodology

#### 4.1. Discrete Wavelet Analysis

#### 4.2. Convolutional Neural Networks

#### 4.3. Hybrid Discrete Wavelet–CNN Method

## 5. Results

#### 5.1. Descriptive Analysis

#### 5.2. Hybrid Discrete Wavelet–CNN Model

#### 5.2.1. Training Parameters

- Epochs = 100.
- Batch size = 4.
- Learning rate = 0.001.
- Optimizer = ADAM.
- Dataset split = 70%, 15%, 15%.

#### 5.2.2. Results

#### 5.3. Discussion

## 6. Conclusions

- It can automatically and effectively classify faults related to short circuits even in indistinguishable cases where white noise and load changes occur.
- It can drastically reduce both the training time and the data volume employed for training the neural network while maintaining competitive accuracy. Therefore, the proposed method could be considered a data compression method as well.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Albrecht, P.F.; Appiarius, J.C.; McCoy, R.M.; Owen, E.L.; Sharma, D.K. Assessment of the Reliability of Motors in Utility Applications—Updated. IEEE Power Eng. Rev.
**1986**, PER-6, 31–32. [Google Scholar] [CrossRef] - Singh, G.K.; Al Kazzaz, S.A.S. Induction machine drive condition monitoring and diagnostic research—A survey. Electr. Power Syst. Res.
**2003**, 64, 145–158. [Google Scholar] [CrossRef] - Karmakar, S.; Chattopadhyay, S.; Mitra, M.; Sengupta, S. Induction Motor and Faults. In Induction Motor Fault Diagnosis. Power Systems; Springer: Singapore, 2016; pp. 7–28. [Google Scholar] [CrossRef]
- Mortazavizadeh, S. A Review on Condition Monitoring and Diagnostic Techniques of Rotating Electrical Machines. Phys. Sci. Int. J.
**2014**, 4, 310–338. [Google Scholar] [CrossRef] - Laamari, Y.; Allaoui, S.; Bendaikha, A.; Saad, S. Fault Detection Between Stator Windings Turns of Permanent Magnet Synchronous Motor Based on Torque and Stator-Current Analysis Using FFT and Discrete Wavelet Transform. Math. Model. Eng. Probl.
**2021**, 8, 315–322. [Google Scholar] [CrossRef] - Theodoropoulos, P.; Spandonidis, C.C.; Fassois, S. Use of Convolutional Neural Networks for vessel performance optimization and safety enhancement. Ocean Eng.
**2022**, 248, 110771. [Google Scholar] [CrossRef] - Spandonidis, C.; Theodoropoulos, P.; Giannopoulos, F.; Galiatsatos, N.; Petsa, A. Evaluation of deep learning approaches for oil & gas pipeline leak detection using wireless sensor networks. Eng. Appl. Artif. Intell.
**2022**, 113, 104890. [Google Scholar] [CrossRef] - Theodoropoulos, P.; Spandonidis, C.C.; Giannopoulos, F.; Fassois, S. A Deep Learning-Based Fault Detection Model for Optimization of Shipping Operations and Enhancement of Maritime Safety. Sensors
**2021**, 21, 5658. [Google Scholar] [CrossRef] - Jayaswal, P.; Wadhwani, A.K. Application of artificial neural networks, fuzzy logic and wavelet transform in fault diagnosis via vibration signal analysis: A review. Aust. J. Mech. Eng.
**2009**, 7, 157–171. [Google Scholar] [CrossRef] - Verma, A.K.; Nagpal, S.; Desai, A.; Sudha, R. An efficient neural-network model for real-time fault detection in industrial machine. Neural Comput. Appl.
**2021**, 33, 1297–1310. [Google Scholar] [CrossRef] - Duan, L.; Hu, J.; Zhao, G.; Chen, K.; Wang, S.X.; He, J. Method of inter-turn fault detection for next-generation smart transformers based on deep learning algorithm. High Volt.
**2019**, 4, 282–291. [Google Scholar] [CrossRef] - Ashfaq, H.; Quadri, M.N. Fault Current Detection of Three Phase Power Transformer Using Wavelet Transform. J. Eng. Res. Appl.
**2013**, 3, 1444–1454. [Google Scholar] - Hussain, M.; Soother, D.K.; Kalwar, I.H.; Memon, T.D.; Memon, Z.A.; Nisar, K.; Chowdhry, B.S. Stator winding fault detection and classification in three-phase induction motor. Intell. Autom. Soft Comput.
**2021**, 29, 869–883. [Google Scholar] [CrossRef] - Hsueh, Y.-M.; Ittangihal, V.R.; Wu, W.-B.; Chang, H.-C.; Kuo, C.-C. Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform. Symmetry
**2019**, 11, 1212. [Google Scholar] [CrossRef] - Wang, J.; Zhuang, J.; Duan, L.; Cheng, W. A multi-scale convolution neural network for featureless fault diagnosis. In Proceedings of the 2016 International Symposium on Flexible Automation (ISFA), Cleveland, OH, USA, 1–3 August 2016; pp. 65–70. [Google Scholar] [CrossRef]
- Agrawal, P.; Jayaswal, P. Diagnosis and Classifications of Bearing Faults Using Artificial Neural Network and Support Vector Machine. J. Inst. Eng. Ser. C
**2020**, 101, 61–72. [Google Scholar] [CrossRef] - Yan, X.; She, D.; Xu, Y. Deep order-wavelet convolutional variational autoencoder for fault identification of rolling bearing under fluctuating speed conditions. Expert Syst. Appl.
**2023**, 216, 119479. [Google Scholar] [CrossRef] - Attallah, O.; Ibrahim, R.A.; Zakzouk, N.E. Fault diagnosis for induction generator-based wind turbine using ensemble deep learning techniques. Energy Rep.
**2022**, 8, 12787–12798. [Google Scholar] [CrossRef] - Mansour, R.F.; Alabdulkreem, E.; Eid, H.F.; K, S.; Khan, M.A.R.; Kumar, A. Fuzzy logic based on-line fault detection and classification method of substation equipment based on convolutional probabilistic neural network with discrete wavelet transform and fuzzy interference. Optik
**2022**, 270, 169956. [Google Scholar] [CrossRef] - Tang, S.; Zhu, Y.; Yuan, S. Intelligent fault identification of hydraulic pump using deep adaptive normalized CNN and synchrosqueezed wavelet transform. Reliab. Eng. Syst. Saf.
**2022**, 224, 108560. [Google Scholar] [CrossRef] - Ahmadipour, M.; Othman, M.M.; Alrifaey, M.; Bo, R.; Ang, C.K. Classification of faults in grid-connected photovoltaic system based on wavelet packet transform and an equilibrium optimization algorithm-extreme learning machine. Meas. J. Int. Meas. Confed.
**2022**, 197, 111338. [Google Scholar] [CrossRef] - Allan, O.A.; Morsi, W.G. A new passive islanding detection approach using wavelets and deep learning for grid-connected photovoltaic systems. Electr. Power Syst. Res.
**2021**, 199, 107437. [Google Scholar] [CrossRef] - Venkatesh, S.N.; Jeyavadhanam, B.R.; Sizkouhi, A.M.; Esmailifar, S.; Aghaei, M.; Sugumaran, V. Automatic detection of visual faults on photovoltaic modules using deep ensemble learning network. Energy Rep.
**2022**, 8, 14382–14395. [Google Scholar] [CrossRef] - Esfetanaj, N.N.; Nojavan, S. The Use of Hybrid Neural Networks, Wavelet Transform and Heuristic Algorithm of WIPSO in Smart Grids to Improve Short-Term Prediction of Load, Solar Power, and Wind Energy. In Operation of Distributed Energy Resources in Smart Distribution Networks; Elsevier: Amsterdam, The Netherlands, 2018; pp. 75–100. [Google Scholar] [CrossRef]
- Guo, T.; Zhang, T.; Lim, E.; Lopez-Benitez, M.; Ma, F.; Yu, L. A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities. IEEE Access
**2022**, 10, 58869–58903. [Google Scholar] [CrossRef] - Fukushima, K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern.
**1980**, 36, 193–202. [Google Scholar] [CrossRef] - Cordeiro, J.R.; Raimundo, A.; Postolache, O.; Sebastião, P. Neural Architecture Search for 1D CNNs—Different Approaches Tests and Measurements. Sensors
**2021**, 21, 7990. [Google Scholar] [CrossRef]

Block Name | Parameter | Value |
---|---|---|

Three-phase squirrel-cage IM (4 kW, 400 V, 50 Hz, 1430 rpm) | Stator resistance | 1.4050 Ω |

Rotor resistance | 1.3590 Ω | |

Stator inductance | 0.005839 H | |

Rotor inductance | 0.005839 H | |

Pole pairs | 2 | |

Friction factor | 0.002985 N m s | |

Inertia | 0.0131 J/kg m² | |

Mutual Inductance | 0.1722 H | |

Three-phase block of fault | Fault resistance | 0.1 Ω |

Ground resistance | 0.01 Ω | |

Snubber resistance | 106 Ω |

Duration (s) | Load Torque (Nm) | Rotational Speed (rpm) |
---|---|---|

0–1 | 0 | 1499 |

1–2 | 26.72 | 1434 |

2–3 | 13.36 | 1468 |

3–4 | 6.68 | 1484 |

4–5 | 0 | 1499 |

Threshold | d5 | Many d | Indistinguishable | Unrecognizable |
---|---|---|---|---|

Level 1 | Level 2 | Level 3 | Level 4 | Level 5 |

Phase A | Phase B | Phase C |
---|---|---|

0.01137 | 0.012 | 0.019 |

Monitoring Parameter | Type | Intensity R (Ω) | Level |
---|---|---|---|

Current of Phase A | A–B | 0.1 | 1 |

A–G | 0.1 | 1 | |

A–C | 0.1 | 1 | |

B–C | 0.1 | 2 | |

B–G | 0.1 | 1 | |

C–G | 0.1 | 1 | |

A–B | 1.0 | 1 | |

A–G | 1.0 | 3 | |

A–B | 5.0 | 3 | |

A–G | 5.0 | 4 | |

Current of Phase B | A–B | 0.1 | 1 |

A–G | 0.1 | 1 | |

A–C | 0.1 | 3 | |

B–C | 0.1 | 1 | |

B–G | 0.1 | 1 | |

C–G | 0.1 | 1 | |

A–B | 1.0 | 1 | |

A–G | 1.0 | 5 | |

A–B | 5.0 | 3 | |

A–G | 5.0 | 5 | |

Current of Phase C | A–B | 0.1 | 3 |

A–G | 0.1 | 3 | |

A–C | 0.1 | 1 | |

B–C | 0.1 | 1 | |

B–G | 0.1 | 1 | |

C–G | 0.1 | 1 | |

A–B | 1.0 | 5 | |

A–G | 1.0 | 4 | |

A–B | 5.0 | 5 | |

A–G | 5.0 | 5 |

Monitoring Parameter | Type | Intensity R (Ω) | Level |
---|---|---|---|

Current of Phase A | A–B | 0.1 | 1 |

A–G | 0.1 | 1 | |

Current of Phase B | A–B | 0.1 | 1 |

A–G | 0.1 | 3 | |

Current of Phase C | A–B | 0.1 | 3 |

A–G | 0.1 | 3 |

Monitoring Parameter | Type | Intensity R (Ω) | Level |
---|---|---|---|

Current of Phase A | A–B | 0.1 | 1 |

A–G | 0.1 | 4 | |

Current of Phase B | A–B | 0.1 | 1 |

A–G | 0.1 | 5 | |

Current of Phase C | A–B | 0.1 | 4 |

A–G | 0.1 | 4 |

Monitoring Parameter | Type | Intensity R (Ω) | Level |
---|---|---|---|

Current of Phase A | A–B | 5.0 | 2 |

A–G | 5.0 | 4 | |

Current of Phase B | A–B | 5.0 | 3 |

A–G | 5.0 | 5 | |

Current of Phase C | A–B | 5.0 | 5 |

A–G | 5.0 | 4 |

Monitoring Parameter | Type | Intensity R (Ω) | Level |
---|---|---|---|

Current of Phase A | A–B | 0.1 | 1 |

A–G | 0.1 | 3 | |

Current of Phase B | A–B | 0.1 | 1 |

A–G | 0.1 | 3 | |

Current of Phase C | A–B | 0.1 | 3 |

A–G | 0.1 | 4 |

Layer | Number | Parameters |
---|---|---|

Conv1D | 2 | Filters = 64, kernel size = 3, activation = ReLU. |

Dropout | 1 | Rate = 0.5. |

MaxPooling1D | 1 | Pool size = 2. |

Flatten Dense 1 | 1 1 | - Units = 100, activation = ReLU. |

Dense 2 | 1 | Units = 3, activation = softmax. |

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## Share and Cite

**MDPI and ACS Style**

Paraskevopoulos, D.; Spandonidis, C.; Giannopoulos, F.
Hybrid Wavelet–CNN Fault Diagnosis Method for Ships’ Power Systems. *Signals* **2023**, *4*, 150-166.
https://doi.org/10.3390/signals4010008

**AMA Style**

Paraskevopoulos D, Spandonidis C, Giannopoulos F.
Hybrid Wavelet–CNN Fault Diagnosis Method for Ships’ Power Systems. *Signals*. 2023; 4(1):150-166.
https://doi.org/10.3390/signals4010008

**Chicago/Turabian Style**

Paraskevopoulos, Dimitrios, Christos Spandonidis, and Fotis Giannopoulos.
2023. "Hybrid Wavelet–CNN Fault Diagnosis Method for Ships’ Power Systems" *Signals* 4, no. 1: 150-166.
https://doi.org/10.3390/signals4010008