School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK.
Central Group, Kitling Road, Knowsley Business Park, Liverpool L34 9JA, UK.
Sensors (Basel). 2022 Apr 20;22(9):3166. doi: 10.3390/s22093166.
Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults. This is costly and often reactive in nature. Real-time monitoring offers a solution for detecting faults without the need for manual observation. However, manual interpretation for threshold anomaly detection is often subjective and varies between industrial experts. This approach is ridged and prone to a large number of false positives. To address this issue, we propose a machine learning (ML) approach to model normal working operations and detect anomalies. The approach extracts key features from signals representing a known normal operation to model machine behaviour and automatically identify anomalies. The ML learns generalisations and generates thresholds based on fault severity. This provides engineers with a traffic light system where green is normal behaviour, amber is worrying and red signifies a machine fault. This scale allows engineers to undertake early intervention measures at the appropriate time. The approach is evaluated on windowed real machine sensor data to observe normal and abnormal behaviour. The results demonstrate that it is possible to detect anomalies within the amber range and raise alarms before machine failure.
旋转机械故障检测系统已经过时，并且依赖于常规测试来发现故障。这既昂贵又往往具有被动性。实时监测为检测故障提供了一种无需手动观察的解决方案。然而，对于阈值异常检测的手动解释往往是主观的，并且在工业专家之间存在差异。这种方法是僵化的，容易出现大量误报。为了解决这个问题，我们提出了一种机器学习 (ML) 方法来模拟正常工作操作并检测异常。该方法从表示已知正常操作的信号中提取关键特征，以模拟机器行为并自动识别异常。ML 学习泛化并根据故障严重程度生成阈值。这为工程师提供了一个信号灯系统，其中绿色表示正常行为，黄色表示令人担忧，红色表示机器故障。该范围允许工程师在适当的时间采取早期干预措施。该方法在窗口化的真实机器传感器数据上进行评估，以观察正常和异常行为。结果表明，在机器故障之前，有可能在黄色范围内检测到异常并发出警报。
