Fault Detection in Rotating Machinery Based on Sound using edge machine learning

1. Fault Detection In Rotating Machinery Based On Sound
Using Edge Machine Learning
V Lalitha

2. Problem Statement
The problem statement of the paper revolves around the need for effective fault detection in rotating
machinery to prevent failures that could lead to life-threatening consequences and incur high maintenance
costs and machine downtime. Traditional methods, relying on the experience of operators, are deemed
inefficient.
The researchers highlight the importance of early-stage fault detection in industrial processes to prevent
failures that could lead to serious consequences and increase maintenance costs.
The identified solution involves the use of machine learning (ML) techniques, particularly edge machine
learning (edge ML), for building a fault diagnosis system based on acoustic emission (AE).
2

3. Introduction
• Background:
Machines are integral to various industries, their efficiency and reliability are continuously improved
through technological advancements.
• Reasons for Machine Failures:
Machines can fail due to factors which include mechanical wear and tear, bearing failure, corrosion.
• Historical Sound Monitoring:
In the past, experienced individuals manually monitored sounds in running machines for fault
diagnosis. However, this approach was subjective and not efficient.
3

4. • Introduction to Conditional Monitoring:
Conditional Monitoring using Artificial Intelligence and Machine Learning techniques have gained
attention for fault diagnosis.
• Proposed Methodology:
The research work is divided into two phases:
a) The development phase, where machine sound signal is acquired using a smartphone and
the remaining steps are done on a non embedded device using MATLAB.
4

5. b) In the second phase, the selected model is deployed and integrated into an embedded device as a
sensor node.
• Data Acquisition:
A smart phone is used as data acquisition to record signals.
The acoustic signals of the drill are captured using a micro-electromechanical systems (MEMS)
microphone.
Image Source: https://ieeexplore.ieee.org/document/10017251 5

6. • Data Preprocessing:
Data Preprocessing is the most important step in fault diagnosis using sound signals since at this stage.
The techniques used were ‘Hanning Window’ and ‘Digital Bandpass Filter’.
• Feature Selection and Extraction:
Features are extracted from the signals in the time domain and frequency domain.
Total 16 features were extracted out of which 6 features from the frequency domain and 10 features are
extracted from time domain.
• Frequency Domain Features:
The frequency domain features are Peak1, Peak2, Peak3, PeakLocs1, PeakLocs2, PeakLocs3.
6

7. ‘’
Time Domain Features:
1. Root Mean Square (RMS)
2. Mean Value
3. Kurtosis
4. Skewness
5. Impulsive Factor
6. Shape Factor
7. Margin Factor
8. Crest Factor
9. Variance
7

8. Image Source: https://ieeexplore.ieee.org/document/10017251
8
• After all features are extracted, a feature selection based on ANOVA is used to rank the 16 features by their
importance. Peak1 is the most important feature, then variance, RMS, Peak3, Peak2, kurtosis, shape
factor, and so on.

9. Methodology
• Usage of edge Machine Learning:
The fault diagnosis is implemented using edge Machine Learning.
Edge machine learning refers to the practice of performing ML tasks on edge devices, which are devices
located close to the data source or endpoint rather than relying on a centralized cloud or data centre.
• The steps followed are :
1. A smart sensor node is built to acquire a sound signal using a MEMS Microphone (MP45DT02)
connected to a microcontroller(STM32F407VG).
9

10. 2. Preprocessing of data is done using MATLAB. The techniques used are Hanning window and
bandpass filter.
3. Feature extraction is also performed using MATLAB. Then, machine learning models are trained using
the extracted features.
4. The implementation phase involves deploying the trained model on an embedded device and MATLAB
coder may be used for code generation to optimize the performance of the features extraction part on the
embedded device.
• The machine learning model created classifies the data into 5 classes namely
‘Off condition with noise’, ‘Healthy condition’, ‘Bearing fault’, ‘Fan fault’, ‘Gear fault’.
10

11. Figure 4: Confusion matrix for the ensemble of bagged trees classifier.
11
5. The embedded device then handles real-time fault detection and classification.
After implementation, the following confusion matrix was obtained:
Image Source: https://ieeexplore.ieee.org/document/10017251

12. Conclusion
• With a fine decision tree model, the system achieves a robust 96.1% accuracy in fault detection,
emphasizing the practical application of edge machine learning.
• Despite a minor accuracy drop during implementation on an embedded device, the approach
demonstrates reliability and efficiency.
• This research underscores the potential of edge machine learning in industrial settings, offering a
valuable framework for early fault detection and prevention of downtime.
• Overall, the methodology presents a practical and efficient solution for fault diagnosis in rotating
machinery using edge machine learning.
12

13. Thank you
13