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Cited by 44Machine Fault Diagnosis TechniquesReliability and Maintenance OptimizationStructural Health Monitoring Techniques

AI-enabled predictive maintenance framework for autonomous mobile cleaning robots

Open access
Pookkuttath, Sathian, Rajesh Elara, Mohan, Sivanantham, Vinu, Ramalingam, Balakrishnan·2021·Sensors
DOI 10.3390/s22010013PMC PMC8747287
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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.

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  • By using AI and ML for predictive maintenance, downtime may be decreased and overall productivity can rise.

    “By doing so, downtime may be decreased and overall productivity can rise [65] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [65]

    The Introduction says manual monitoring may cause extended downtime and that automated predictive maintenance overcomes those pitfalls; it also describes an ML-based approach intended to avoid unplanned downtime. The paper does not state that overall productivity rises, and its Conclusions report fault-classification performance rather than measured downtime or productivity gains. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • When industrial equipment is predicted to fail, maintenance may be carried out before a breakdown happens thanks to the usage of AI and ML.

    “When industrial equipment is predicted to fail, maintenance may be carried out before a breakdown happens thanks to the usage of AI and ML.”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [65]

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Cited by

Sorted by citations

Authors
Pookkuttath, S., Rajesh Elara, M., Sivanantham, V., & Ramalingam, B.
Affiliations
  • Singapore University of Technology and Design (SG)
  • Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore
Corresponding author
Ramalingam, Balakrishnan
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2021-12-21
Volume
22
Issue
1
Pages
13
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s22010013
PMC
PMC8747287
OpenAlex
W4200111540
Cited by
44
References
1
Citations by year
2026: 9 · 2025: 13 · 2024: 13 · 2023: 9
FWCI
2.37
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Robotics Programme under its Robotics Enabling Capabilities and Technologies (Funding Agency Project No. 192 25 00051, 192 22 00058, 192 22 00108)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
  • Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
Keywords
predictive maintenance, vibration signals, performance degradation detection, operational safety, autonomous robots, 1D convolutional neural network, inertial measurement unit, structural imbalance, SLAM mapping
MeSH terms
Algorithms, Artificial Intelligence, Robotics, Vibration

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