Fault identification model using IIoT for industrial application

Kumar, T. Ch. Anil; Sharma, Neeta Bhusal; Mishra, Amit Kumar; Patil, Pravin P.; Reddy, S. Sarveswara; Bhardwaj, Ramakant

doi:10.1016/j.measen.2022.100526

Cited by 2 publications

(1 citation statement)

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“…The fault diagnosis system ( Figure 9 ) detects faults based on changes in features over time. The fault diagnosis system works with the client–server architecture in the IT network [ 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. The features are extracted from raw measurements and data or pre-processed data.…”

Section: General Structure Of Fault Diagnosis and Perspective Mainten...mentioning

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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