Utilizing data mining to influence maintenance actions

For Aircraft Launch and Recovery Equipment (ALRE), the goal is to get planes in the air and ensure they land safely. Consequently, a high operational availability (Ao) is crucial to ALRE operations. In order to ensure high Ao, it is crucial that the amount of maintenance, both corrective and preventative, is kept to a minimum. Historically, improvements have been reactive in nature to satisfy the Fleet's needs of the moment and are never implemented across the Fleet. One approach to improving maintenance practices is to use historical data in combination with data mining to determine where and how maintenance procedures can be changed or enhanced. For example, if a maintenance manual says to remove three electronics boxes based on a built-in test (BIT) code, but historically, the data shows that removing and replacing two of the boxes never fix the problem, then the maintainer can be directed to first remove and replace the box which the data suggests is the most-likely cause of failure. This type of improvement is where data mining can be used to enhance or modify maintenance procedures. The Integrated Support Environment (ISE) team and the Integrated Diagnostics and Automated Test Systems (IDATS) team of NAVAIR Lakehurst are jointly investigating the use of data mining as an important tool to enhance ALRE systems and to potentially decrease preventive maintenance on-board Navy vessels, thereby reducing the total cost of ownership. The authors' approach is to use maintenance actions, system performance data, and supply information to draw a clear picture of the failures, diagnoses and repair actions for specific components of ALRE systems. The authors are using a commercial off-the-shelf (COTS) data mining suite, called SPSS Clementine, alongside custom software tools to detect the meaningful, yet hidden, patterns within the mountain of data associated with ALRE systems. SPSS Clementine is one of the data mining industry's premier tools, allowing rapid development of models for data mining. Additionally, ALRE subject matter experts (SMEs) were consulted to ensure the validity of the teams' findings. The combination of modern data mining practices and expert knowledge of ALRE systems will be leveraged to improve the maintenance performed at the O-level and to possibly understand why the failure happened in the first place. This paper will describe the forthcoming investigation exemplifying how the data warehouse holding various sources of data about ALRE systems will be utilized to improve the education of maintainers and to enhance maintenance practices, to understand the cause of component failures, as well as provide solutions to diagnose these failures. Utilizing the knowledge and expertise of database systems and data mining which the ISE team provides, combined with SME knowledge, non-trivial solutions to ALRE maintenance practices shall be uncovered to improve the maintenance environment on-ship.