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. 2021 Dec 21;22(1):13.
doi: 10.3390/s22010013.

AI-Enabled Predictive Maintenance Framework for Autonomous Mobile Cleaning Robots

Sathian Pookkuttath  1 , Mohan Rajesh Elara  1 , Vinu Sivanantham  1 , Balakrishnan Ramalingam  1
Affiliations

AI-Enabled Predictive Maintenance Framework for Autonomous Mobile Cleaning Robots

Sathian Pookkuttath et al. Sensors (Basel). .

Abstract

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.

Keywords: 1D CNN; artificial intelligence; deep learning; mobile cleaning robot; predictive maintenance; vibration source classification.

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Conflict of interest statement

There are no conflict of interest.

Figures

Figure 1
Figure 1
Overview of the proposed DL-based PdM framework.
Figure 2
Figure 2
Autonomous steam mopping robot ‘Snail’.
Figure 3
Figure 3
Vibration source classification—Normal and Potential source of failure.
Figure 4
Figure 4
Data acquisition system and Linear-rotational motion of the Snail robot.
Figure 5
Figure 5
1D CNN Structure.
Figure 6
Figure 6
Robot test set up for vibration data collection of five classes.
Figure 7
Figure 7
Vibration signals—Normal class.
Figure 8
Figure 8
Vibration signals—Terrain class.
Figure 9
Figure 9
Vibration signals—Collision class.
Figure 10
Figure 10
Vibration signals—Assembly class.
Figure 11
Figure 11
Vibration signals—Structure class.
Figure 12
Figure 12
Real time field test case studies.
See this image and copyright information in PMC

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