Faculty of Automatic Control, Robotics and Electrical Engineering, Poznan University of Technology, 60-965 Poznań, Poland.
Sensors (Basel). 2023 Apr 5;23(7):3755. doi: 10.3390/s23073755.
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.
以人为本且具有弹性的欧洲产业 5.0 需要机器具有较长的使用寿命，以减少电子废物。处理这个问题的恰当方法是应用能够远程检测、隔离和识别故障的诊断系统。作者提出使用 HTTP/1.1 协议进行批处理作为故障诊断服务器。微控制器 HTTP 客户端以 JSON 格式将数据发送到诊断服务器。此外，还使用 MQTT 协议将微控制器客户端的流（微批次）数据发送到两个故障诊断客户端。第一个故障诊断 MQTT 客户端仅使用频率数据进行评估。作者对标准快速傅里叶变换（FFT）的增强是使用滑动离散傅里叶变换（rSDFT、mSDFT、gSDFT 和 oSDFT），它允许根据时域中的新样本和频域中的先前结果递归地更新频谱。这种方法可以降低计算成本。第二个 MQTT 客户端的故障诊断方法使用短时傅里叶变换（STFT）将 IMU 6 DOF 传感器数据转换为六个频谱图，然后将它们组合成一个 RGB 图像。所有三轴加速度计和三轴陀螺仪数据都用于获得时频 RGB 图像。机器的诊断是通过适合 RGB 图像识别的训练卷积神经网络来执行的。预测结果以包含每个状态的预测状态和概率的 JSON 对象返回。对于 HTTP，故障诊断结果作为响应发送，对于 MQTT，将其发送到预测主题。这两种协议和两种提出的方法都适用于基于旋转机器机械振动的故障诊断，并在演示中进行了测试。
