By Gbemiga Olamikan.
The Chief of the Air Staff, Air Marshal Hasan Abubakar, has declared that the Nigerian Air Force (NAF) is adopting predictive maintenance culture across all NAF Engineering and Maintenance units as it is designed to assist in determining the condition of in-service aircraft and other equipment to estimate, ahead of time, when maintenance should be performed.
He stated this while addressing personnel of NAF Logistics Command, Lagos, during his operational tour of NAF units in the Lagos area. He said this approach will enable cost savings over routine or time-based proactive or preventive maintenance culture as the advantage of predictive maintenance is that, it allows convenient scheduling of corrective maintenance and prevents unexpected equipment failures. He said that, with the right data of equipment lifetime, the tendency for increased plant safety, fewer accidents with negative impact on environment, and optimized spare parts handling is assured.
According to him, “We have moved away from proactive maintenance culture to predictive maintenance. This has been possible because we have continued to keep up-to-date data about our spares and current maintenance status of all our platforms. With this, it will enable us to provide spares before they are due for maintenance. It will also reduce downtime while expediting aircraft serviceability.”
However, CAS Air Marshal Hasan Abubakar explains that, there is need to continually sustain and enhance NAF’s safety standards as it is now compulsory in all NAF training institutions to review their curriculums to include all aspects of safety training.
He explained that Predictive maintenance differs from proactive maintenance because it relies on the actual condition of equipment, rather than average or expected life statistics, to predict when maintenance will be required. Some of the main components that are necessary for implementing predictive maintenance are data collection and preprocessing, early fault detection, fault detection, time to failure predictions, maintenance scheduling and resource optimization.
