LightFD: Real-Time Fault Diagnosis with Edge Intelligence for Power Transformers

Fu, Xinhua; Yang, Kejun; Liu, Min; Tong, Xing; Wu, Chase Q.

doi:10.3390/s22145296

Cited by 4 publications

(4 citation statements)

References 62 publications

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“…STFT changes a one-dimensional time series signal into a two-dimensional time and frequency [ 143 , 144 , 145 ]. The STFT parameters are as follows: time window shape (e.g., rectangle, Hamming [ 146 , 147 ], Hanning [ 148 , 149 ]), step size or overlap of next time window that influence time resolution, time window length that influence frequency resolution

, where

—signal length in samples and

—sampling frequency. Selection of time window length is a trade-off between good time localization or good frequency localization of the sinusoidal/cosinusoidal components.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity

Łuczak

Brock

Siembab

2023

Sensors

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The human-centric and resilient European industry called Industry 5.0 requires a long lifetime of machines to reduce electronic waste. The appropriate way to handle this problem is to apply a diagnostic system capable of remotely detecting, isolating, and identifying faults. The authors present usage of HTTP/1.1 protocol for batch processing as a fault diagnosis server. Data are sent by microcontroller HTTP client in JSON format to the diagnosis server. Moreover, the MQTT protocol was used for stream (micro batch) processing from microcontroller client to two fault diagnosis clients. The first fault diagnosis MQTT client uses only frequency data for evaluation. The authors’ enhancement to standard fast Fourier transform (FFT) was their usage of sliding discrete Fourier transform (rSDFT, mSDFT, gSDFT, and oSDFT) which allows recursively updating the spectrum based on a new sample in the time domain and previous results in the frequency domain. This approach allows to reduce the computational cost. The second approach of the MQTT client for fault diagnosis uses short-time Fourier transform (STFT) to transform IMU 6 DOF sensor data into six spectrograms that are combined into an RGB image. All three-axis accelerometer and three-axis gyroscope data are used to obtain a time-frequency RGB image. The diagnosis of the machine is performed by a trained convolutional neural network suitable for RGB image recognition. Prediction result is returned as a JSON object with predicted state and probability of each state. For HTTP, the fault diagnosis result is sent in response, and for MQTT, it is send to prediction topic. Both protocols and both proposed approaches are suitable for fault diagnosis based on the mechanical vibration of the rotary machine and were tested in demonstration.

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, where

—signal length in samples and

—sampling frequency. Selection of time window length is a trade-off between good time localization or good frequency localization of the sinusoidal/cosinusoidal components.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity

Łuczak

Brock

Siembab

2023

Sensors

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“…Fu et al [51] has proposed a fast lightweight fault diagnosis method for power transformers, called LightFD, which integrates several technical components.…”

Section: Related Workmentioning

confidence: 99%

Fault Detection and Prediction for Power Transformers Using Fuzzy Logic and Neural Networks

Mateus,

Farinha,

Mendes

2024

Energies

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Transformers are indispensable in the industry sector and society in general, as they play an important role in power distribution, allowing the delivery of electricity to different loads and locations. Because of their great importance, it is necessary that they have high reliability, so that their failure does not cause additional losses to the companies. Inside a transformer, the primary and secondary turns are insulated by oil. Analyzing oil samples, it is possible to diagnose the health status or type of fault in the transformer. This paper combines Fuzzy Logic and Neural Network techniques, with the main objective of detecting and if possible predicting failures, so that the maintenance technicians can make decisions and take action at the right time. The results showed an accuracy of up to 95% in detecting failures. This study also highlights the importance of predictive maintenance and provides a unique approach to support decision-making for maintenance technicians.

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“…SVM classifier is used for fault diagnosis in UAV motors. Moreover, Cai et al [162] and Fu et al [168] did their research on anomaly detection in transformers using acoustics. Fu and team developed a method namely lightFD to perform SVM classification on edge devices with limited computing power.…”

Section: A Machinery Fault Detectionmentioning

confidence: 99%

A Survey on Artificial Intelligence-Based Acoustic Source Identification

Zaheer,

Ahmad,

Habibi

et al. 2023

IEEE Access

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The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions.

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LightFD: Real-Time Fault Diagnosis with Edge Intelligence for Power Transformers

Cited by 4 publications

References 62 publications

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity

Fault Detection and Prediction for Power Transformers Using Fuzzy Logic and Neural Networks

A Survey on Artificial Intelligence-Based Acoustic Source Identification

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