Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China.
Hebei Key Laboratory of Condition Monitoring and Assessment of Mechanical Equipment, Shijiazhuang 050003, China.
Sensors (Basel). 2022 May 20;22(10):3884. doi: 10.3390/s22103884.
It is critical to deploy wireless data transmission technologies remotely, in real-time, to monitor the health state of diesel engines dynamically. The usual approach to data compression is to collect data first, then compress it; however, we cannot ensure the correctness and efficiency of the data. Based on sparse Bayesian optimization block learning, this research provides a method for compression reconstruction and fault diagnostics of diesel engine vibration data. This method's essential contribution is combining compressive sensing technology with fault diagnosis. To achieve a better diagnosis effect, we can effectively improve the wireless transmission efficiency of the vibration signal. First, the dictionary is dynamically updated by learning the dictionary using singular value decomposition to produce the ideal sparse form. Second, a block sparse Bayesian learning boundary optimization approach is utilized to recover structured non-sparse signals rapidly. A detailed assessment index of the data compression effect is created. Finally, the experimental findings reveal that the approach provided in this study outperforms standard compression methods in terms of compression efficiency and accuracy and its ability to produce the desired fault diagnostic effect, proving the usefulness of the proposed method.
远程、实时地部署无线数据传输技术来动态监测柴油机的健康状态至关重要。通常的数据压缩方法是先采集数据，然后再进行压缩；然而，我们无法保证数据的正确性和效率。基于稀疏贝叶斯优化块学习，本研究提出了一种压缩重建和柴油机振动数据故障诊断的方法。该方法的重要贡献是将压缩感知技术与故障诊断相结合。为了达到更好的诊断效果，可以有效提高振动信号的无线传输效率。首先，通过奇异值分解学习字典，动态更新字典，生成理想的稀疏形式。其次，利用块稀疏贝叶斯学习边界优化方法快速恢复结构非稀疏信号。创建了数据压缩效果的详细评估指标。最后，实验结果表明，本研究提出的方法在压缩效率和准确性以及产生所需故障诊断效果方面均优于标准压缩方法，证明了所提出方法的有效性。
