A Data Mining based Approach for Electric Motor Anomaly Detection Applied on Vibration Data

Egaji, Oche A.; Ekwevugbe, Tobore; Griffiths, Mark

doi:10.1109/worlds450073.2020.9210318

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…Numerous TinyML use cases share a commonality—a significant amount of data. For instance, an electric motor anomaly detection system, as detailed in [ 6 ], could be implemented using a TinyML device. This device would conduct data harvesting and analysis on-board, transmitting detected anomalies promptly.…”

Section: Related Workmentioning

confidence: 99%

Study of the Impact of Data Compression on the Energy Consumption Required for Data Transmission in a Microcontroller-Based System

Piątkowski,

Puślecki,

Walkowiak

2023

Sensors

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As the number of Internet of Things (IoT) devices continues to rise dramatically each day, the data generated and transmitted by them follow similar trends. Given that a significant portion of these embedded devices operate on battery power, energy conservation becomes a crucial factor in their design. This paper aims to investigate the impact of data compression on the energy consumption required for data transmission. To achieve this goal, we conduct a comprehensive study using various transmission modules in a severely resource-limited microcontroller-based system designed for battery power. Our study evaluates the performance of several compression algorithms, conducting a detailed analysis of computational and memory complexity, along with performance metrics. The primary finding of our study is that by carefully selecting an algorithm for compressing different types of data before transmission, a significant amount of energy can be saved. Moreover, our investigation demonstrates that for a battery-powered embedded device transmitting sensor data based on the STM32F411CE microcontroller, the recommended transmission module is the nRF24L01+ board, as it requires the least amount of energy to transmit one byte of data. This module is most effective when combined with the LZ78 algorithm for optimal energy and time efficiency. In the case of image data, our findings indicate that the use of the JPEG algorithm for compression yields the best results. Overall, our research underscores the importance of selecting appropriate compression algorithms tailored to specific data types, contributing to enhanced energy efficiency in IoT devices.

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Section: Related Workmentioning

confidence: 99%

Study of the Impact of Data Compression on the Energy Consumption Required for Data Transmission in a Microcontroller-Based System

Piątkowski,

Puślecki,

Walkowiak

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In [3], anomaly detection techniques using machine learning models such as K-Nearest Neighbour (KNN), Support Vector Regression (SVR) and Random Forest (RF) have been applied to vibration data for early fault detection of industrial electric motors. According to the authors the Random Forest presented the best performance compared to SVR and KNN, based on less number of false positives and the detection time.…”

Section: Related Workmentioning

confidence: 99%

Automatic Anomaly Detection in Vibration Analysis Based on Machine Learning Algorithms

Torres,

Ramalho,

Correia

2022

Innovations in Mechatronics Engineering II

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This paper presents an approach for automatic anomaly detection through vibration analysis based on machine learning algorithms. The study focuses on induction motors in a predictive maintenance context, but can be applied to other domains. Vibration analysis is an important diagnostic tool in industrial data analysis to predict anomalies caused by equipment defects or in its use, allowing to increase its lifetime. It is not a new technique and is widely used in the industry, however with the Industry 4.0 paradigm and the need to digitize any process, it gains relevance to automatic fault detection. The Isolation Forest algorithm is implemented to detect anomalies in vibration datasets measured in an experimental apparatus composed of an induction motor and a coupling system with shaft alignment/misalignment capabilities. The results show that it is possible to detect anomalies automatically with a high level of precision and accuracy.

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“…Analyzing vibration data to detect equipment anomalies constitutes a widely employed approach, given that equipment failures often manifest prominently in vibration data. Numerous scholars have thus far applied anomaly detection methods based on vibration signals to diverse categories of rotating machinery, encompassing gas turbines [5][6][7][8]. This practice underscores the effectiveness of utilizing vibration data for identifying potential anomalies in machinery operation.…”

Section: Introductionmentioning

confidence: 99%

Gas Turbine Anomaly Detection under Time-Varying Operation Conditions Based on Spectra Alignment and Self-Adaptive Normalization

Miao,

Feng,

Xiao

et al. 2024

Sensors

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Gas turbine vibration data may exhibit considerable differences under time-varying conditions, which poses challenges for neural network anomaly detection. We first propose a framework for a gas turbine vibration frequency spectra process under time-varying operation conditions, assisting neural networks’ ability to capture weak information. The framework involves scaling spectra for aligning all frequency components related to rotational speed and normalizing frequency amplitude in a self-adaptive way. Degressive beta variational autoencoder is employed for learning spectra characteristics and anomaly detection, while a multi-category anomaly index is proposed to accommodate various operating conditions. Finally, a dataset of blade Foreign Object Damage (FOD) fault occurring under time-varying operating conditions was used to validate the framework and anomaly detection. The results demonstrate that the proposed method can effectively reduce the spectra differences under time-varying conditions, and also detect FOD fault during operation, which are challenging to identify using conventional methods.

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A Data Mining based Approach for Electric Motor Anomaly Detection Applied on Vibration Data

Cited by 8 publications

References 17 publications

Study of the Impact of Data Compression on the Energy Consumption Required for Data Transmission in a Microcontroller-Based System

Study of the Impact of Data Compression on the Energy Consumption Required for Data Transmission in a Microcontroller-Based System

Automatic Anomaly Detection in Vibration Analysis Based on Machine Learning Algorithms

Gas Turbine Anomaly Detection under Time-Varying Operation Conditions Based on Spectra Alignment and Self-Adaptive Normalization

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