Institute of IR4.0, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia.
Department of Electrical and Electronics Engineering, Faculty of Engineering and Technology, Tunku Abdul Rahman University College, Kampus Utama, Jalan Genting Kelang, Kuala Lumpur 53300, Malaysia.
Sensors (Basel). 2022 Aug 23;22(17):6321. doi: 10.3390/s22176321.
The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data collected is not useful. This systematic review's main goal is to synthesize the existing evidence on the application of predictive maintenance (PdM) with visual aids and to identify the key knowledge gaps in areas including utilities, power generation, industry, and energy consumption. After a thorough search and evaluation for relevancy, 37 documents were identified. Moreover, we identified the visual analytics of PdM, including anomaly detection, planning/scheduling, exploratory data analysis (EDA), and explainable artificial intelligence (XAI). The findings revealed that anomaly detection was a major domain in PdM-related works. We conclude that most of the literature lacks depth in terms of an overall framework that combines data-driven and knowledge-driven techniques of PdM in the manufacturing industry. Some works that utilized both techniques indicated promising results, but there is insufficient research on involving maintenance personnel's feedback in the latter stage of PdM architecture. Thus, there are still pertinent issues that need to be investigated, and limitations that need to be overcome before PdM is deployed with minimal human involvement.
在制造业生产设施中广泛采用网络物理系统和其他前沿数字技术可能会促使利益相关者接受工业 4.0 的理念。一些工业公司已经在他们的机器上安装了不同的传感器；但是，如果没有适当的分析，收集的数据就没有用。本系统评价的主要目的是综合现有的关于使用预测性维护（PdM）和视觉辅助的证据，并确定包括公用事业、发电、工业和能源消耗在内的各个领域的关键知识空白。经过彻底的搜索和相关性评估，确定了 37 份文件。此外，我们确定了 PdM 的视觉分析，包括异常检测、规划/调度、探索性数据分析（EDA）和可解释的人工智能（XAI）。研究结果表明，异常检测是 PdM 相关工作中的一个主要领域。我们得出的结论是，大多数文献在将数据驱动和知识驱动的 PdM 技术结合到制造业的整体框架方面缺乏深度。一些同时使用这两种技术的作品表明了有希望的结果，但在 PdM 架构的后期阶段涉及维护人员的反馈方面，研究还不够充分。因此，在 PdM 实现最小化人工干预之前，仍有一些相关问题需要调查，一些局限性需要克服。
