AI-Driven Predictive Maintenance of Industrial Fire Protection Machinery: A Reliability-Based Engineering Framework for Saudi Manufacturing Facilities

Industrial fire protection machinery has a very particular reliability requirement in that it has to be capable of performing when required, even though it will be in standby for the vast majority of its life. The conventional approach of scheduled inspection is still absolutely vital, though periodic testing may well miss the gradual degradation that occurs between inspections. This paper is a critical review of the potential for AI to augment rather than supplant the existing inspection and reliability engineering practices for fire pumps, drivers, valves, controllers and the associated suppression system in the Saudi manufacturing environment. It is a structured, interpretive synthesis of the peer-reviewed literature published between 2020 and 2025 on predictive maintenance, fire protection equipment, fault diagnosis, physics-informed prognostics and risk based decision making. The proposed contribution is a demand aware engineering framework which links operational signals, proof test results, component degradation, uncertainty and consequence severity to auditable maintenance decisions. Particular emphasis is placed on low frequency failures, environmental exposure, the unequal consequences of various types of failure and the difference between diagnostic accuracy and demonstrated system availability. The review identifies tangible opportunities in the area of multi-modal monitoring, hybrid reliability models and explainable maintenance prioritisation, whilst also highlighting the continuing problems with data portability, causal attribution, model calibration and field validation. A staged evaluation programme is recommended prior to any safety critical deployment. The key point is that prediction only becomes of operational value when it improves the evidence of readiness on demand and does not in any way compromise the requirement for mandatory proof testing or human responsibility.