Condition-Based Maintenance Optimization in Industrial Systems

Condition-based maintenance (CBM) optimisation harnesses real-time data and advanced analytics to schedule maintenance only when equipment condition indicates impending degradation. By integrating sensors, Internet of Things architectures and prognostic algorithms, CBM moves beyond fixed schedules to a demand-driven paradigm that minimises unplanned downtime, reduces life-cycle costs and prolongs asset life. Key components include health indicators derived from vibration, thermal or cycle-time measurements, data-fusion platforms that aggregate multisensor streams and decision-support models which weigh maintenance costs against failure risks. Recent advances embrace digital twins, edge-computing frameworks and human–machine collaboration to refine fault-prediction accuracy and to tailor interventions across diverse asset fleets. Globally, optimised CBM underpins sustainable manufacturing, power generation and transportation systems by improving reliability, energy efficiency and resource utilisation while accommodating the complexity of Industry 4.0 and emerging Industry 5.0 environments.

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Research from all publishers

Guidelines for supervision frameworks have been proposed to streamline CBM deployment by detailing processes for experimental data collection, critical-item selection and failure-reproduction methods. These design principles have been validated through two industrial case studies, illustrating how a structured supervision solution can guide sensor placement, threshold setting and workflow integration. A comprehensive state-of-the-art review of CBM highlights the evolution of mathematical models and platform architectures, emphasising standardisation efforts that enable consistent implementation across sectors. It identifies emerging trends such as digital-twin integration and recommends modular platform designs to accelerate uptake. In mining operations, a cost-effective wireless accelerometer network combined with machine learning classification has delivered early warning of deteriorating conditions in non-stationary mobile machinery. By measuring three-axis vibration spectra in real time and mapping them to severity states, the system has detected “unacceptable” conditions up to 170 hours before failure, demonstrating practical gains in availability and safety for heavy-duty fleets.

Condition-Based Maintenance Optimization in Industrial Systems publication trend

The graph below shows the total number of articles in condition-based maintenance optimization in industrial systems across all publications each year (not limited to Nature Index journals).

Technical terms

Condition-Based Maintenance (CBM): Maintenance strategy that schedules interventions according to continuous monitoring of equipment health.

Predictive maintenance: Data-driven approach that forecasts the timing of failures or degradation to plan interventions in advance.

Virtual sensor: Software model that infers equipment condition from indirect or existing control signals rather than dedicated physical sensors.

Mean Time To Repair (MTTR): Average duration required to restore equipment to full operation following a detected fault.

References

1. Design of supervision solutions for industrial equipment: Schemes, tools and guidelines for the user. Journal of Industrial Information Integration (2024).
2. Condition-Based Monitoring and Maintenance: State of the Art Review. Applied Sciences (2022).
3. Data-Driven Condition Monitoring of Mining Mobile Machinery in Non-Stationary Operations Using Wireless Accelerometer Sensor Modules. IEEE Access (2021).