Development and Analysis of AI-Driven Anomaly Detection and Predictive Maintenance Algorithms for Robotic Systems in Industrial Environments: Leveraging Electrical and Sensor Data
Aug 2026· Cureus Journal of Computer Science· Vol 3· 0 citations· 30 references
TL;DR
This study finds no evidence that attention-enhanced temporal autoencoders offer a universal advantage over simpler deep learning baselines for industrial robotic predictive maintenance; any benefit appears concentrated in detecting subtle, temporally extended fault signatures, is small in magnitude, and was not confirmed as statistically significant in the present sample.
Abstract
Industrial robotic systems are essential to modern manufacturing, but their reliability is threatened by progressive mechanical and electrical degradation. Traditional reactive and preventive maintenance strategies are inadequate for the complex, high-dimensional sensor environments of contemporary industrial robots. This study developed and evaluated AE-LSTM-ATT (Attention-Enhanced Hybrid LSTM Autoencoder), an LSTM encoder-decoder architecture with Bahdanau-style additive attention, for unsupervised anomaly detection in industrial robotic systems. The model was evaluated on the public Industrial Robot Anomaly Detection (IndRAD) dataset (robot joint positions, velocities, torques, and motor currents) against an attention-free LSTM Autoencoder and an Isolation Forest baseline, across five independent training runs and four synthetically injected anomaly types (spike, step, freeze-to-zero, and freeze-to-last-value). AE-LSTM-ATT and the attention-free baseline performed comparably on abrupt anomalies (spike, step; AUC-ROC (Area Under the Receiver Operating Characteristic Curve) = 1.000 for both). For the subtler, gradually manifesting anomaly types, attention provided a small, directionally positive advantage: for freeze-to-last-value, AE-LSTM-ATT achieved AUC-ROC = 0.518 ± 0.059 and F1-score = 0.682 ± 0.020 (95% CI, 5 seeds), versus 0.491 ± 0.046 and 0.670 ± 0.017 for the baseline; freeze-to-zero showed overlapping confidence intervals. An independent replication of this comparison with a paired significance test across five matched seeds (section "Ablation: Contribution of the Attention Mechanism") did not find the difference to be statistically significant, and the direction of the per-seed difference was not uniform. These results indicate that any attention-related benefit in this architecture is, at most, small and anomaly-type dependent, and is not established as statistically significant at the sample sizes evaluated here; attention showed no distinguishable advantage for abrupt, high-amplitude faults already near-perfectly separable by reconstruction error alone. The framework operates in an unsupervised paradigm requiring no labelled fault data, supporting deployment in settings where run-to-failure data are scarce. Overall, this study finds no evidence that attention-enhanced temporal autoencoders offer a universal advantage over simpler deep learning baselines for industrial robotic predictive maintenance; any benefit appears concentrated in detecting subtle, temporally extended fault signatures, is small in magnitude, and was not confirmed as statistically significant in the present sample.
An end-to-end predictive maintenance system is proposed for high stress mechanical drivetrain and rotating machinery and the architecture proposed combines an industrial Internet of Things edge sensory network and hybrid machine learning and deep learning pipelines.
Ashish Kumar, Md Mohtab Alam, N. Priya et al.· International journal of com...· 0 citations
The rapid emergence of Industry 4.0 technologies has significantly transformed manufacturing industries by
integrating Artificial Intelligence (AI), Industrial Internet of Things (IIoT), Cloud Computing, Big Data Analytics, and CyberPhysical Systems. Among these advancements, predictive maintenance has emerged as one of the most promising applications
for improving operational efficiency and equipment reliability. Traditional maintenance strategies, such as corrective and
preventive maintenance, often lead to increased operational costs, unnecessary maintenance activities, and unexpected
equipment failures. Consequently, organizations are increasingly adopting AI-powered predictive maintenance systems that
utilize deep learning techniques to predict machine failures before they occur. Deep learning models, including Convolutional
Neural Networks (CNN), Long Short-Term Memory (LSTM), Autoencoders, Recurrent Neural Networks (RNN), and
Transformer-based architectures, have demonstrated remarkable capabilities in analyzing large volumes of industrial sensor
data and identifying hidden patterns associated with equipment degradation. This study provides a comprehensive review of AIpowered predictive maintenance using deep learning approaches, examining its applications, benefits, challenges, and future
opportunities. The study further proposes a conceptual framework integrating AI, IIoT, and deep learning technologies to
improve maintenance decision-making. The findings indicate that deep learning significantly enhances fault diagnosis,
Remaining Useful Life (RUL) prediction, anomaly detection, and maintenance optimization. However, challenges such as data
quality issues, model interpretability, cybersecurity concerns, and integration complexities continue to influence industrial
adoption. The study concludes that AI-powered predictive maintenance will become a fundamental component of smart
manufacturing and Industry 5.0 initiatives.
M. Varusai Mohamed, N. Malathi, P. Vanithamani et al.· International Journal for Re...· 0 citations
This study develops and evaluates an AI-based analytical system for detecting anomalies in industrial processes. The work reviews major sources of risk in industrial control systems, distinguishes point, contextual, and collective anomalies, and summarizes the principal machine-learning approaches used for industrial anomaly detection. A synthetic dataset modeled on the Secure Water Treatment (SWaT) testbed was created with 10 sensor and actuator variables and 10,000 one-second observations, including 1,000 anomalous samples. After missing-value interpolation, duplicate removal, low-variance filtering, and standardization for consistent analysis and visualization, an Isolation Forest with 200 trees was trained in a novelty-detection configuration using normal operating data. On the held-out test set, the model achieved 88.63% accuracy, 45.86% precision, 75.67% recall, and an F1-score of 57.11%. The results show that Isolation Forest can detect most simulated anomalies, although the relatively low precision indicates a substantial false-alarm burden. Future work should validate the approach on authorized real SWaT or PLC-SCADA data, investigate hybrid temporal models, and incorporate explainable-AI methods to support operator decision-making.
Mehdiyeva Almaz, Ahmedov Elmar, Uzakov Gulom et al.· 2026 International Conferenc...· 0 citations
Mobile robots, like the ultra-flat overrunable (UFO) robot platform, used in automotive active safety tests, currently lack self-diagnostic capabilities necessary to detect present hardware defects. This circumstance can lead to more severe failures, causing expensive repairs and operational downtime. This work proposes, for the first time, a reconstructionbased time-series anomaly detection model for these mobile robots, considering defect classes such as unevenly worn full-rubber tires or damaged dampers. Unlike prior publications, the proposed approach leverages the vast quantities of unlabeled data generated during routine operation through a simple pre-training step. Furthermore, it optimizes the hyperparameters of the implemented gated recurrent unit-based variational autoencoder (GRU-VAE) and evaluates both a stateless, windowed training approach and one using truncated backpropagation through time (TBPTT). The model's generalization capabilities are demonstrated by successfully detecting six defect types, with four of them not present in the data used for hyperparameter optimization and threshold selection. This is validated using a test set collected from five system instances at various points over a period of several months, achieving an F1 score of 0.936, indicating strong practical viability.
Henrik Meyer, Karsten Raguse, A. W. Colombo et al.· arXiv.org· 0 citations
This study presents a comprehensive research framework for real-time embedded AI-based industrial robot monitoring that combines edge computing, embedded deep learning, multi-sensor fusion, anomaly detection, predictive maintenance, and intelligent decision-making and provides a scalable, energy-efficient, and intelligent monitoring solution suitable for next-generation smart factories and Industry 5.0 environments.
Corrado Böhm, Corrado Gini· International Journal of Int...· 0 citations
The stability and reliability of temperature sensors in industrial cooling systems are critical to process quality, energy efficiency, and operational safety. However, existing approaches lack systematic stability metrics and intelligent predictive capabilities. This study proposes an AI-driven framework integrating stability feature engineering with machine learning models for fault identification and early prediction of temperature sensors in power plant cooling systems. The framework introduces three physics-based stability indicators—rolling standard deviation (σ_roll), variation intensity index (VII), and short-term variation magnitude (ΔT_short)—to quantify sensor signal quality. These features, combined with operational parameters, are used to train support vector machine (SVM) and Long Short-Term Memory (LSTM) models for binary classification. The framework is validated using over 260,000 one-minute records per unit collected from three parallel steam-turbine generating units (Units 1, 2, and 3) of the same coastal thermal power plant. Each unit is served by an independent once-through seawater cooling loop instrumented with redundant Pt-100 temperature sensors at the inlet and outlet manifolds; the three units differ in their operating profile—Unit 1 operates under variable load with frequent cold-start events, Unit 2 under moderate variable load, and Unit 3 under stable high-load conditions—with data collected at 1 min intervals from January to June 2025. Under an explicitly anomaly-positive evaluation, with the full confusion matrix reported for every unit and model, classification performance is limited and strongly unit-dependent. In real-time identification, AUC-based ranking ability varies across units (SVM AUC = 0.65, 0.75, and 0.98 for Units 1–3; LSTM AUC = 0.66, 0.31, and 0.52), but under the extreme class imbalance (anomaly rate ≈ 0.07–0.13% in the test partitions), the calibrated operating-point precision and F1-scores remain low for all unit–model combinations (F1 ≤ 0.26, MCC ≤ 0.28). McNemar’s test indicates statistically significant paired differences for Units 1 and 2 but not for Unit 3. These results show that, on this dataset, neither model attains reliable anomaly classification, and that all reported metrics must be interpreted together with the disclosed confusion-matrix counts and severe class imbalance. The primary contribution of the framework is therefore methodological—physics-based stability indicators, redundant sensor cross-checking, and an operational false-alarm analysis—rather than high-accuracy prediction, and the study highlights the difficulty of learning-based prediction for rare, rule-defined thermal sensor anomalies.
D. Chen, Jung-Chieh Wang, Bo-Siang Chen· Information· 0 citations
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