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Applied Sciences
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AI-Based Machinery Health Monitoring
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AI-Based Machinery Health Monitoring

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 30 December 2025 | Viewed by 746

Special Issue Editors

Dr. Alexandru Stanciu

E-Mail Website
Guest Editor
National Institute for Research and Development in Informatics–ICI Bucharest, 011455 Bucharest, Romania
Interests: distributed systems; cybersecurity; machine learning; artificial intelligence
Dr. Rui Araújo

E-Mail Website
Guest Editor
Institute of Systems and Robotics (ISR-UC), Department of Electrical and Computer Engineering (DEEC-UC), University of Coimbra, Pólo II, PT-3030-290 Coimbra, Portugal
Interests: computational intelligence; intelligent control; computational learning; machine learning; fuzzy systems; neural networks; optimization; modeling; simulation; estimation; prediction; control; big data; robotics; mobile robotics and intelligent vehicles; robot manipulators control; sensing; soft sensors; automation; industrial systems; embedded systems; real-time systems; general architectures and systems for controlling robot manipulators; mobile robots
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in artificial intelligence are driving a paradigm shift in machinery health monitoring, enabling proactive maintenance and increased operational efficiency across diverse industries. By harnessing real-time sensor data, machine learning techniques, and cutting-edge technologies such as digital twins, AI-based monitoring systems can detect early signs of degradation, accurately diagnose faults, and predict failures before they lead to costly downtime. This holistic approach—combining intelligent data analytics at the cloud and edge, sophisticated anomaly detection methods, and new forms of human–machine collaboration—represents a transformative leap from traditional time-based maintenance toward truly predictive and prescriptive strategies.

This Special Issue seeks original research articles, case studies, and review papers covering the latest advancements and future trends in AI-based machinery health monitoring. Potential topics include, but are not limited to:

  • Machine learning, deep learning, and hybrid techniques for fault diagnosis and prognostics.
  • Large Language Models (LLMs), agenting workflows and frameworks for anomaly detection, predictive maintenance, and intelligent fault diagnosis.
  • Advanced sensing technologies, Internet of Things (IoT) integration, and edge/cloud architectures.
  • Digital twin development for real-time monitoring and “what-if” simulations.
  • Remaining useful life (RUL) estimation and performance optimization.
  • Applications of augmented/virtual reality in maintenance tasks.
  • Industry 4.0/5.0 concepts and sustainable, energy-efficient maintenance solutions.

Manuscripts must be original, high-quality contributions that have not been previously published or are not under consideration elsewhere. Extended versions of conference papers are welcome, provided they demonstrate significant new content and clear improvements over the original work.

We look forward to receiving your contributions.

Dr. Alexandru Stanciu
Dr. Rui Araújo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • predictive maintenance
  • fault diagnosis and prognostics
  • advanced sensing and monitoring techniques
  • digital twins
  • remaining useful life prediction
  • deep learning
  • machine learning

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Published Papers (1 paper)

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Research

28 pages, 5699 KiB  
Article
Multi-Modal Excavator Activity Recognition Using Two-Stream CNN-LSTM with RGB and Point Cloud Inputs
by Hyuk Soo Cho, Kamran Latif, Abubakar Sharafat and Jongwon Seo
Appl. Sci. 2025, 15(15), 8505; https://doi.org/10.3390/app15158505 - 31 Jul 2025
Viewed by 217
Abstract
Recently, deep learning algorithms have been increasingly applied in construction for activity recognition, particularly for excavators, to automate processes and enhance safety and productivity through continuous monitoring of earthmoving activities. These deep learning algorithms analyze construction videos to classify excavator activities for earthmoving [...] Read more.
Recently, deep learning algorithms have been increasingly applied in construction for activity recognition, particularly for excavators, to automate processes and enhance safety and productivity through continuous monitoring of earthmoving activities. These deep learning algorithms analyze construction videos to classify excavator activities for earthmoving purposes. However, previous studies have solely focused on single-source external videos, which limits the activity recognition capabilities of the deep learning algorithm. This paper introduces a novel multi-modal deep learning-based methodology for recognizing excavator activities, utilizing multi-stream input data. It processes point clouds and RGB images using the two-stream long short-term memory convolutional neural network (CNN-LSTM) method to extract spatiotemporal features, enabling the recognition of excavator activities. A comprehensive dataset comprising 495,000 video frames of synchronized RGB and point cloud data was collected across multiple construction sites under varying conditions. The dataset encompasses five key excavator activities: Approach, Digging, Dumping, Idle, and Leveling. To assess the effectiveness of the proposed method, the performance of the two-stream CNN-LSTM architecture is compared with that of single-stream CNN-LSTM models on the same RGB and point cloud datasets, separately. The results demonstrate that the proposed multi-stream approach achieved an accuracy of 94.67%, outperforming existing state-of-the-art single-stream models, which achieved 90.67% accuracy for the RGB-based model and 92.00% for the point cloud-based model. These findings underscore the potential of the proposed activity recognition method, making it highly effective for automatic real-time monitoring of excavator activities, thereby laying the groundwork for future integration into digital twin systems for proactive maintenance and intelligent equipment management. Full article
(This article belongs to the Special Issue AI-Based Machinery Health Monitoring)
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