Predictive Maintenance Through Multisource Industrial Data Fusion

Olivia Bennett

Predictive Maintenance Through Multisource Industrial Data Fusion

Author(s)	Dr. Olivia Bennett
Country	United States
Abstract	Predictive maintenance increasingly depends on the ability to convert heterogeneous industrial data into reliable information about equipment health, fault progression, and remaining useful life. Conventional condition-monitoring systems frequently rely on a single sensor type or isolated operational variable, although industrial degradation normally manifests through multiple physical and contextual channels. Vibration, acoustic emission, temperature, pressure, electrical current, rotational speed, lubricant condition, process variables, maintenance histories, production records, environmental conditions, machine-controller events, and operator observations may each reveal different aspects of asset deterioration. This study examines multisource industrial data fusion as an enabling architecture for predictive maintenance. A structured integrative review of recent predictive-maintenance, multi-sensor fault-diagnosis, multimodal artificial-intelligence, digital-twin, and explainable-AI research was undertaken. The evidence indicates that fusion can be implemented at data, feature, representation, decision, and contextual levels. Contemporary studies demonstrate measurable advantages of combining complementary sensor streams, while recent field-oriented research illustrates the additional value of integrating internal machine signals with operating context. However, greater data volume does not automatically generate superior maintenance predictions. Sensor synchronization, missing observations, noise, contradictory signals, data drift, domain shifts, class imbalance, communication latency, uncertainty, and explainability remain major implementation challenges. The paper therefore proposes a Multisource Predictive Maintenance Fusion Framework comprising synchronized acquisition, data-quality control, hierarchical fusion, health-state estimation, uncertainty-aware prognosis, explainable decision support, and maintenance feedback. A recent published ablation study is used to illustrate the incremental contribution of contextual information: a full multisource predictive-maintenance model obtained a macro F1-score of 0.855, compared with 0.829 after environmental context was removed and 0.807 when only internal mechanical information was used. These particular results are simulation-domain evidence and are not presented as original findings of this paper. The study concludes that multisource data fusion contributes most effectively to predictive maintenance when complementary information is aligned by physical meaning, uncertainty is preserved rather than concealed, predictions are validated under changing operating conditions, and maintenance engineers remain able to understand and challenge automated recommendations.
Keywords	Predictive Maintenance; Multisource Data Fusion; Multi-Sensor Fusion; Industrial IoT; Fault Diagnosis; Remaining Useful Life; Condition Monitoring; Explainable AI; Digital Twin; Industry 4.0
Field	Engineering
Published In	Volume 2, Issue 1, January-February 2021
Published On	2021-02-24

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American Journal of Advanced Multidisciplinary Innovation and Research

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Predictive Maintenance Through Multisource Industrial Data Fusion

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