AI and Industrial IoT join forces to power next-gen predictive maintenance

In AIoT-based systems, sensors continuously collect high-frequency data such as vibration, temperature, pressure, and electrical signals. These data streams are processed by machine learning and deep learning models that can detect anomalies, estimate remaining useful life, and recommend prescriptive actions. Unlike earlier centralized systems, AIoT distributes intelligence across edge devices, gateways, and cloud platforms, allowing decisions to be made closer to where data are generated.

Predictive maintenance, long positioned as a cost-saving upgrade over reactive repairs, is now being recast as a strategic pillar of Industry 5.0, where resilience, sustainability, and human-centric design take precedence over automation alone. A new large-scale scientific review argues that this transition is being driven by the rapid maturation of the Artificial Intelligence of Things, or AIoT, which integrates AI-driven analytics directly into industrial IoT ecosystems.

In the study Artificial Intelligence of Things for Next-Generation Predictive Maintenance, published in Sensors, researchers map technologies, architectures, and deployment models that collectively signal a move away from isolated machine monitoring toward distributed, intelligent, and adaptive maintenance systems aligned with Industry 5.0 priorities.

From reactive repairs to intelligent, human-centric maintenance systems

Earlier maintenance models focused on reactive repairs or scheduled interventions based on fixed intervals. While these approaches reduced catastrophic failures, they often led to unnecessary servicing, resource waste, and unplanned downtime. Predictive maintenance introduced data-driven forecasting, using sensor inputs to anticipate failures before they occur. However, the review shows that traditional predictive maintenance systems struggle to scale in modern industrial environments characterized by heterogeneous equipment, massive data volumes, and real-time operational demands.

AIoT addresses these limitations by tightly coupling artificial intelligence with the Industrial Internet of Things. In AIoT-based systems, sensors continuously collect high-frequency data such as vibration, temperature, pressure, and electrical signals. These data streams are processed by machine learning and deep learning models that can detect anomalies, estimate remaining useful life, and recommend prescriptive actions. Unlike earlier centralized systems, AIoT distributes intelligence across edge devices, gateways, and cloud platforms, allowing decisions to be made closer to where data are generated.

According to the review, this architectural shift is essential for Industry 5.0, which focuses on resilience and human oversight rather than full automation. By reducing latency and enabling localized decision-making, AIoT systems support safer operations and faster responses to emerging faults. Human operators remain part of the loop, interpreting AI-driven insights rather than being replaced by them. This marks a departure from Industry 4.0’s automation-first logic and aligns maintenance strategies with worker well-being, system robustness, and sustainability goals.

Notably, the study shows that AIoT-based predictive maintenance is no longer experimental. Across manufacturing, transportation, and energy sectors, deployed systems are already delivering measurable improvements in equipment availability, cost reduction, and operational continuity. These gains are achieved not through a single breakthrough model but through the coordinated integration of sensing, analytics, and system design.

AI techniques and architectures redefine predictive accuracy and scalability

At the technical level, the study provides an extensive synthesis of AI methods used in next-generation predictive maintenance. Traditional machine learning techniques such as support vector machines, random forests, and k-nearest neighbors remain relevant for structured data and constrained environments. However, the review documents a clear shift toward deep learning models capable of handling complex, high-dimensional sensor data.

Recurrent neural networks, long short-term memory models, convolutional networks, and transformer-based architectures are increasingly used to capture temporal patterns and cross-sensor dependencies. These models significantly improve fault detection and remaining useful life estimation, particularly in systems with nonlinear degradation behavior. The study also highlights the growing role of generative models, including variational autoencoders and hybrid architectures, which enhance feature extraction and robustness under noisy conditions.

Yet the authors are careful to note that performance gains come with trade-offs. Deep and transformer-based models often demand substantial computational resources, making them difficult to deploy directly on edge devices. This challenge has driven the rise of edge–cloud collaborative architectures, a central focus of the review. In these systems, lightweight models operate at the edge to perform real-time anomaly detection, while more complex analytics and model training occur in the cloud.

Federated learning emerges as a particularly important innovation. By allowing models to be trained across distributed devices without sharing raw data, federated learning addresses privacy, security, and data ownership concerns that have hindered large-scale industrial AI deployment. The study shows that federated approaches can maintain high predictive accuracy while reducing communication overhead and exposure of sensitive operational data.

Despite these advances, the review identifies persistent gaps. Interpretability remains a major challenge, especially in safety-critical environments where operators must understand why a system recommends a particular maintenance action. Model drift, caused by changing operating conditions and equipment aging, threatens long-term reliability. Data heterogeneity across vendors and legacy systems complicates integration, particularly in brownfield industrial sites.

These limitations, the authors argue, are not merely technical but systemic. Solving them requires coordinated progress in model design, system architecture, and organizational practices.

Industry 5.0 sets new expectations for trust, resilience, and sustainability

Rather than treating AIoT as a purely efficiency-driven upgrade, the authors frame it as an enabler of broader industrial transformation. Industry 5.0 prioritizes human-centricity, resilience to disruption, and environmental sustainability, demanding that technological systems support these values.

The review finds that AIoT-based predictive maintenance can directly contribute to sustainability by reducing waste, extending asset lifespans, and optimizing resource use. By intervening only when needed, predictive systems lower energy consumption and material usage associated with unnecessary maintenance. In energy and transportation sectors, improved reliability also supports the transition to greener infrastructures by minimizing unplanned outages and inefficiencies.

Resilience is another key theme. Distributed AIoT systems are inherently more robust than centralized architectures, as they can continue operating even when parts of the network fail. Edge intelligence allows critical decisions to be made locally during connectivity disruptions, a capability increasingly important in the face of climate events, supply chain shocks, and cyber threats.

Human-centricity, however, remains the most challenging dimension. The study notes that many existing AIoT deployments focus heavily on technical performance while underexploring human factors such as operator trust, usability, and skill requirements. Interfaces that overwhelm users with opaque predictions risk undermining confidence and adoption. The authors argue that explainable AI, intuitive visualization, and participatory system design are essential to ensure that human operators remain empowered rather than sidelined.

Cybersecurity also features prominently in the analysis. While many studies mention encryption and secure communication, comprehensive security architectures are often lacking. As AIoT systems expand the attack surface of industrial operations, integrating robust security and privacy safeguards becomes a prerequisite for trustworthy deployment.

A roadmap for next-generation predictive maintenance

The study outlines future directions for AIoT-enabled predictive maintenance. Key priorities include the development of lightweight, explainable models suitable for edge deployment; standardized benchmarks to enable fair comparison across systems; and longitudinal studies that validate performance in real operational environments rather than controlled testbeds.

The authors also call for interdisciplinary collaboration. Engineers, data scientists, human factors specialists, and policymakers must work together to align technical innovation with regulatory and ethical frameworks.