# US 2025/0093231 A1 · System and method to detect anomaly in mechanical components

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- Number: US 2025/0093231 A1
- Document ID: US20250093231A1
- Status: Published application
- Kind: A1
- Application number: 18/889,456
- Filed: 2024-09-19
- Published: 2025-03-20
- Earliest claimed: 2023-09-20
- Assignees: Pes University
- Applicants: PES University
- Inventors: Ananya Mungara; Tarakesh Mogallapalli; Manjunath G; Namratha V. Reddy; Immanuel Calvin D
- Web page: https://patentgrep.com/patent/US20250093231A1

## CPC

- G01M 7/00: Vibration-testing of structures; Shock-testing of structures (G01M9/00 takes precedence)
- G01M 13/00: Testing of machine parts
- G01M: TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR

## Abstract

A method to detect anomaly in mechanical components based on sound, vibration, and temperature analysis is disclosed. The method includes receiving sound, vibration, and temperature data from the mechanical components in real-time. Further, the method includes generating synthesized sound data from the received sound data based on varying noise environments by performing pitch changing, temporal stretching, and noise injection. Furthermore, the method includes preprocessing the received data and the synthesized sound data to remove background noise and thermal shifting. Moreover, the method includes extracting one or more features from the preprocessed data. Additionally, the method includes identifying anomaly in the mechanical components based on the extracted one or more features by employing one or more Machine Learning (ML) models. The method also includes rendering at least the identified anomaly in the mechanical components to a user.

## Claims

### Claim 1

A system to detect anomaly in mechanical components based on sound, vibration, and temperature analysis, the system comprising:
a receiver module to receive sound data, vibration data, and temperature data from the mechanical components in real-time in various operational conditions;
a synthesizer to generate synthesized sound data from the received sound data based on varying noise environment by performing at least one of: pitch changing, temporal stretching, and noise injection;
a pre-processor to preprocess the received data and the synthesized sound data, to remove background noise and thermal shifting;
a feature extraction module to extract one or more features from the preprocessed received data and the preprocessed synthesized data;
an anomaly identification module to identify anomaly in the mechanical components based on the extracted one or more features by employing one or more Machine Learning (ML) models; and
a rendering module to render at least the identified anomaly in the mechanical components to a user.

### Claim 2 (depends on 1)

The system of claim 1, wherein the synthesizer is further configured to convert the vibration to an audio signal and corresponding sound data, such that the received sound data and the sound data corresponding to the vibration data are analyzed together.

### Claim 3 (depends on 1)

The system of claim 1, wherein the synthesizer further enhances dataset by applying one or more data augmentation techniques on both received data and synthetic data.

### Claim 4 (depends on 1)

The system of claim 1, wherein the preprocessing is performed at least one of: normalization, filtering, equalization, and noise reduction.

### Claim 5 (depends on 1)

The system of claim 1, wherein the preprocessing further includes utilizing Non-Local Means (NLM) filtering to reduce noise, such that target signal is determined by locating and processing related audio patches.

### Claim 6 (depends on 1)

The system of claim 1, wherein the preprocessing further includes reducing noise signals by statistical models including at least one of: Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs).

### Claim 7 (depends on 1)

The system of claim 1, wherein the preprocessing further includes changing audio spectrum information for balancing desired audio signal and undesirable background sound by utilizing at least one of: high-pass, low-pass, band-pass, and notch filters.

### Claim 8 (depends on 1)

The system of claim 1, wherein the one or more features are associated with at least one of: sinusoidal modulation features, time domain features, frequency domain features, time-frequency domain features, rhythm and temporal features, statistical features, Mel-Frequency Cepstal Coefficients (MFCCs), harmonic and timbral features, and waveform shape features.

### Claim 9 (depends on 1)

The system of claim 1, wherein the one or more ML models include at least one of: Deep Neural Networks (DNN), Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Recurrent Neural Networks (RNNs).

### Claim 10 (depends on 1)

The system of claim 1, wherein the anomaly identification module is further configured to identify a solution for the identified anomaly by employing the one or more ML models, such that the rendering module also renders the identified solution to the user.

### Claim 11

A method to detect anomaly in mechanical components based on sound and vibration analysis, the system comprising:
receiving sound data, vibration data, and temperature data from the mechanical components in real-time in various operational conditions;
generating synthesized sound data from the received sound data based on varying noise environment by performing at least one of: pitch changing, temporal stretching, and noise injection;
preprocessing the received data and the synthesized sound data to remove background noise and thermal shifting;
extracting one or more features from the preprocessed received data and the preprocessed synthesized data;
identifying anomaly in the mechanical components based on the extracted one or more features by employing one or more Machine Learning (ML) models; and
rendering at least the identified anomaly in the mechanical components to a user.

### Claim 12 (depends on 11)

The method of claim 11, wherein the synthesizer is further configured to convert the vibration data to an audio signal and corresponding sound data, such that the received sound data and the sound data corresponding to the vibration are analyzed together.

### Claim 13 (depends on 11)

The method of claim 11, wherein the synthesizer further enhances dataset by applying one or more data augmentation techniques on both received data and synthetic data.

### Claim 14 (depends on 11)

The method of claim 11, wherein the preprocessing is performed at least one of: normalization, filtering, equalization, and noise reduction.

### Claim 15 (depends on 11)

The method of claim 11, wherein the preprocessing further includes utilizing Non-Local Means (NLM) filtering to reduce noise, such that target signal is determined by locating and processing related audio patches.

### Claim 16 (depends on 11)

The method of claim 11, wherein the preprocessing further includes reducing noise signals by statistical models including at least one of: Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs).

### Claim 17 (depends on 11)

The method of claim 11, wherein the preprocessing further includes changing audio spectrum information for balancing desired audio signal and undesirable background sound by utilizing at least one of: high-pass, low-pass, band-pass, and notch filters.

### Claim 18 (depends on 11)

The method of claim 11, wherein the one or more features are associated with at least one of: sinusoidal modulation features, time domain features, frequency domain features, time-frequency domain features, rhythm and temporal features, statistical features, Mel-Frequency Cepstal Coefficients (MFCCs), harmonic and timbral features, and waveform shape features.

### Claim 19 (depends on 11)

The method of claim 11, wherein the one or more ML models include at least one of: Deep Neural Networks (DNN), Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Recurrent Neural Networks (RNNs).

### Claim 20 (depends on 11)

The method of claim 11, wherein the anomaly identification module is further configured to identify a solution for the identified anomaly by employing the one or more ML models, such that the rendering module also renders the identified solution to the user.