Design and Development of Lab scale system for condition monitoring of rotating components for machines
Condition Monitoring is an effective way of maintaining the health of a machine. As rotatory machines are an essential part of many industrial processes, the main aim of condition monitoring is to inspect machines to avoid predictable defects through early detection. For the said purpose, vibration-
Project Title
Design and Development of Lab scale system for condition monitoring of rotating components for machines
Project Area of Specialization
Project Summary
Condition Monitoring is an effective way of maintaining the health of a machine. As rotatory machines are an essential part of many industrial processes, the main aim of condition monitoring is to inspect machines to avoid predictable defects through early detection. For the said purpose, vibration-based signals, a widely accepted approach, is used to collect signals from the faulty machine components, the data is analyzed, interpreted and diagnosed through several parameters and techniques such as time domain, time-frequency domain, FFT, artificial neural networks and fuzzy logic. In the proposed work, we worked on the design and development of lab-scale for condition monitoring of bearing faults. This will involve the development of data acquisition system for collecting data, experimental analysis for data collection, and development of computational framework development for processing and analyzing data.
Project Objectives
The objective of the proposed work is to detect the faults in the bearing with the help of vibration signals. The following is a list of objectives for the current work: - Design of a data acquisition system for a table-top system for condition monitoring. - Perform experiments for the generation of vibrational data for various rotating components. - Selection and implementation of an appropriate condition monitoring machine learning technique for a particular rotating component (rotating component will be defined in SOR). - Testing of the machine learning (ML) algorithm for detecting the types of defects.
Project Implementation Method
The vibration signals will be detected from bearings, both healthy and faulty by inducing different types of defects. We will work with the existing fabricated structure and vibrations from bearings will be considered. We habe used jupyter to do python working and machine learning is incorporated.
Benefits of the Project
Condition monitoring is leading the world into new changes as it makes it easy to analyze the data of machines, this makes it an integral part of industries. The benefit behind this lab scale condition monitoring apparatus is to gain exposure and understanding of the industry-related work as this is normally not covered in university teachings.
Technical Details of Final Deliverable
-collection of vibration signals data for defected and healthy bearings
-training of machine learning model to detect faults in bearings
-deploying the condition monitoring system on the already developed test bed.
Final Deliverable of the Project
Core Industry
Other Industries
Core Technology
Other Technologies
Sustainable Development Goals
Required Resources
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| sensor | Equipment | 1 | 1000 | 1000 |
| arduino | Equipment | 1 | 1500 | 1500 |
| Total in (Rs) | 2500 |
If you need this project, please contact me on contact@adikhanofficial.com
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