Pookkuttath Sathian, Rajesh Elara Mohan, Sivanantham Vinu, Ramalingam Balakrishnan
Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore.
Sensors (Basel). 2021 Dec 21;22(1):13. doi: 10.3390/s22010013.
Vibration is an indicator of performance degradation or operational safety issues of mobile cleaning robots. Therefore, predicting the source of vibration at an early stage will help to avoid functional losses and hazardous operational environments. This work presents an artificial intelligence (AI)-enabled predictive maintenance framework for mobile cleaning robots to identify performance degradation and operational safety issues through vibration signals. A four-layer 1D CNN framework was developed and trained with a vibration signals dataset generated from the in-house developed autonomous steam mopping robot 'Snail' with different health conditions and hazardous operational environments. The vibration signals were collected using an IMU sensor and categorized into five classes: normal operational vibration, hazardous terrain induced vibration, collision-induced vibration, loose assembly induced vibration, and structure imbalanced vibration signals. The performance of the trained predictive maintenance framework was evaluated with various real-time field trials with statistical measurement metrics. The experiment results indicate that our proposed predictive maintenance framework has accurately predicted the performance degradation and operational safety issues by analyzing the vibration signal patterns raised from the cleaning robot on different test scenarios. Finally, a predictive maintenance map was generated by fusing the vibration signal class on the cartographer SLAM algorithm-generated 2D environment map.
振动是移动清洁机器人性能下降或操作安全问题的一个指标。因此，在早期阶段预测振动源将有助于避免功能损失和危险的操作环境。这项工作提出了一个基于人工智能（AI）的移动清洁机器人预测性维护框架，以通过振动信号识别性能下降和操作安全问题。开发了一个四层一维卷积神经网络（1D CNN）框架，并使用从内部开发的具有不同健康状况和危险操作环境的自主蒸汽拖地机器人“Snail”生成的振动信号数据集进行训练。使用惯性测量单元（IMU）传感器收集振动信号，并将其分为五类：正常操作振动、危险地形引起的振动、碰撞引起的振动、部件松动引起的振动和结构不平衡振动信号。通过各种实时现场试验和统计测量指标对训练好的预测性维护框架的性能进行了评估。实验结果表明，我们提出的预测性维护框架通过分析清洁机器人在不同测试场景下产生的振动信号模式，准确地预测了性能下降和操作安全问题。最后，通过将振动信号类别融合到制图师同步定位与地图构建（SLAM）算法生成的二维环境地图上，生成了一个预测性维护地图。
