| Idea Name: |
PdM 4.0 |
| Slogan: |
IIoT based industrial predictive maintenance |
| Supervisor Name: |
Dr. Arshad Aziz |
| Supervisor Designation: |
Professor |
| Supervisor School: |
PNEC |
| Supervisor Department: |
Electronics and Power Engineering |
| Contact number: |
+923332228300 |
| Email ID: |
[email protected] |
| Abstract: |
| PdM 4.0 helps in minimizing system downtime. As a result, an induction motor diagnosis system using Motor Current Signature Analysis (MCSA) along with data processing technique is proposed. MCSA is a technique for motor diagnosis using readings from stator current. Sophisticated algorithms & methods for signal processing are suggested to diagnose motor defects that reduce motor's performance. |
| What is the unmet need in society that your idea will fulfill ? |
| Faulty motor has high CO2 emission & current that causes it to heat up. Industries also have to deal with issues like unforeseen motor faults. Finding problems and fixing it in these circumstances needs a lot of time and money. As a result, the project's goal is to use MCSA to assess motor's health. |
| Who needs it ? How many would benefit ? |
| Abrupt consequences' motor cost turns out to be greater than the actual motor cost. By displaying fault intensity on a GUI, this project will alert industries in advance as to what malfunction the motor may face in future. Therefore, industries will then have plenty of time to fix these faults. |
| How will the solution works |
| Data of current signal is obtained & converted to frequency domain by edge processing on Raspberry Pi. Then, this data is sent to cloud via MQTT protocol where messaging broker Mosquitto receives it & Kafka streamlines it. Next, Kafka sends it to TimescaleDB. Later, it goes to analytical engine Apache Spark that discerns faulty & unfaulty values by comparing data to previously fed formulae of faults. Filtered readings are resent to DB & then to GUI Prometheus that shows future motor faults. |
| Who are your competitors ? How is your solution different |
| Firms like Procheck, Adash, Artesis & Samotics also provide MCSA based solution. However, this FYP is superior as it provides a cost effective, cloud-based solution & more defects up front. Additionally, it calculates the motor's RUL using a training model on the analytical engine Apache Spark.
|
| Status: |
new |
| Entry Date & Time: |
2022-12-14 (1142) |
