Health Monitoring of Three Phase Induction Motor Using Current Signature Analysis and Deep Learning Algorithms
Induction motors possess one of the most important roles industrially and commercially. The developing faults in the motors can become catastrophic, if remain unanalyzed. This project presents an effective and novel solution to diagnose the major mechanical faults at the early possibl
2025-06-28 16:32:51 - Adil Khan
Health Monitoring of Three Phase Induction Motor Using Current Signature Analysis and Deep Learning Algorithms
Project Area of Specialization Artificial IntelligenceProject SummaryInduction motors possess one of the most important roles industrially and commercially. The developing faults in the motors can become catastrophic, if remain unanalyzed. This project presents an effective and novel solution to diagnose the major mechanical faults at the early possible stage by utilizing two efficient condition monitoring techniques
to effectively deploy the strategies for the predictive maintenance. Primarily it employs the MCSA (Motor Current Signature Analysis), in which the faults are located by the spectral analysis of the particular harmonic components in the line current at specific characteristic frequencies generated by specific faults as the unique rotating flux. Deep Learning has also been utilized, which assesses severity of the fault and operating condition of machine. The induction machine is prepared with a unique design for the implementation of faults. Specifically in this project we’ve analyzed bearing faults that are detected and localized in the results along with the severity assessment of the operational condition due to induced faults.
- The goal of this project id to design and develop a condition monitoring system for a 3-
phase induction motor using current signature analysis and vibration analysis using deep
learning. - To design and develop condition monitoring system using current sensors and accelerometers.
- To analyze current signatures using spectral monitoring techniques such as FFT and STFT.
- To create a machine learning model of vibration data to for fault prediction using deep neural network.
- Development of embedded monitoring system using FPGA + Processor, current sensors and accelerometers with high-rate data converters.
- Development of embedded software for high-speed data acquisition.
- Induction motors test-bed development for testing and validating results.
- Data-logging and compilation for post-processing.
- Enhancement of industrial maintenance strategy to predictive maintenance.
- Reduce downtime and costs of maintenance
- Efficient and effective monitoring of operational motors
- A complete embedded monitoring system with high-speed data acquisition of current and vibration data.
- Embedded fault monitoring and detection mechanism using current signature analysis.
- Data-logging of motor parameters (current and vibration data) for building deep learning models.
- A real-time inference mechanism on vibration data to predict remaining useful life (RUL) of the machine.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 74280 | |||
| 1HP 3 Phase Induction Motor with Customized Modification | Equipment | 1 | 28000 | 28000 |
| Iron Base | Equipment | 2 | 3500 | 7000 |
| Bearing Housing | Equipment | 2 | 1200 | 2400 |
| Fly Wheel | Equipment | 1 | 3500 | 3500 |
| Winding (Copper) | Equipment | 1 | 2000 | 2000 |
| Bearings (6303) | Equipment | 5 | 85 | 425 |
| Shaft | Equipment | 2 | 300 | 600 |
| Current transformer | Equipment | 3 | 920 | 2760 |
| Accelerometer ADXL335 | Equipment | 1 | 450 | 450 |
| Three phase relay | Equipment | 2 | 450 | 900 |
| Passive components | Equipment | 15 | 10 | 150 |
| Active components | Equipment | 7 | 15 | 105 |
| Audio jacks and terminals | Equipment | 4 | 15 | 60 |
| Veroboard | Equipment | 2 | 80 | 160 |
| Metal standoffs | Equipment | 4 | 25 | 100 |
| Headers | Equipment | 2 | 20 | 40 |
| 2 HP 3 phase induction motor with customised modification | Equipment | 1 | 20000 | 20000 |
| ACS712 | Miscellaneous | 4 | 320 | 1280 |
| 3 Phase Relay | Miscellaneous | 1 | 350 | 350 |
| 3 Phase Power Supply | Miscellaneous | 1 | 4000 | 4000 |