Table 2.

Best practices for PHM systems.

Best Practice
CBA and Dependability Analyses CBA is based on ROI or other metrics Optimum maintenance tasks are based on failure modes and consequences
Data Requirements and Management PHM data is efficiently integrated into the company business process Flexible “middleware” software should minimize hardware and software infrastructure dependencies Open system architectures follow standards and allow updates with knowledge bases and algorithms Data fields are populated automatically History of each component is monitored within the entire logistics system Parts for maintenance are automatically ordered to eliminate excess inventory
Measurement Techniques Maintenance technologies are more connected with design technologies Data are collected early in the life cycle that improves current and future programs Devices utilize built-in-test and expendable devices for failure detection Parameters that are precursors to impending failure are monitored COTS aid integration of maintenance and logistics information systems Pre-processing routines utilize sensor health validation and data de-noising
Diagnostics and Prognostics Features are extracted as condition indicators for accurate and reliable fault diagnosis and failure prognosis Method modules are reusable for broad application to future PHM systems Methodology for incipient failure detection has a specified degree of confidence and given false alarm rate Quick prognostics are based on comparison of condition indicators and a look-up table Prognostic methods are designed with flexibility for data from multiple sources
Testing and Training PHM systems are verified and validated; prognostics model is trained and then tested with different data PHM systems incorporate the “human factor”, expert knowledge PHM system is accepted and utilized by trained personnel