Shandookh Ahmed Adnan, Farhan Ogaili Ahmed Ali, Al-Haddad Luttfi A
Mechanical Engineering Department, University of Technology- Iraq, Baghdad, Iraq.
Mechanical Engineering Department, University of Mustansiriyah, Baghdad, Iraq.
Heliyon. 2024 Jul 6;10(13):e34202. doi: 10.1016/j.heliyon.2024.e34202. eCollection 2024 Jul 15.
Predictive maintenance to avoid fatigue and failure enhances the reliability of mechanics, herewith, this paper explores vibrational time-domain data in advancing fault diagnosis of predictive maintenance. This study leveraged a belt-drive system with the properties: operating rotational speeds of 500-2000 RPM, belt pretensions at 70 and 150 N, and three operational cases of healthy, faulty and unbalanced, which leads to 12 studied cases. In this analysis, two one-axis piezoelectric accelerometers were utilized to capture vibration signals near the driver and pulley. Five advanced statistics were calculated during signal processing, namely Variance, Mean Absolute Deviation (MAD), Zero Crossing Rate (ZCR), Autocorrelation Coefficient, and the signal's Energy. The Taguchi method was used to test the five selected features on the basis of Signal-to-Noise (S/N) ratio. For classifications, an expert system was used based on artificial intelligence where a Random Forest (RF) model was trained on untraditional parameters for optimizing the accuracy. The resulted 0.990 and 0.999, accuracy and AUC, demonstrate the RF model's high dependability. Evidently, the methodology highlights the features potential when progressed into expert systems, which advances predictive maintenance strategies for belt-drive systems.
通过预测性维护来避免疲劳和故障可提高机械的可靠性，因此，本文探讨了振动时域数据在推进预测性维护故障诊断方面的应用。本研究利用了一个皮带传动系统，其特性如下：运行转速为500 - 2000转/分钟，皮带预紧力为70牛和150牛，以及健康、故障和不平衡三种运行工况，由此产生了12个研究案例。在该分析中，使用了两个单轴压电加速度计来采集驱动器和皮带轮附近的振动信号。在信号处理过程中计算了五个高级统计量，即方差、平均绝对偏差（MAD）、过零率（ZCR）、自相关系数和信号能量。田口方法基于信噪比用于测试所选的五个特征。对于分类，使用了基于人工智能的专家系统，其中随机森林（RF）模型针对非传统参数进行训练以优化准确性。所得的准确率0.990和AUC 0.999证明了RF模型的高可靠性。显然，该方法突出了在引入专家系统时这些特征的潜力，从而推进了皮带传动系统的预测性维护策略。
