The CSM implements advanced diagnostic technologies for machinery health monitoring and supports basic and applied research in CSM technologies related to diagnostics and prognostics for electromechanical systems. These systems include rotating components, weapons system platforms, and machinery networks. The CSM Department uses proven analysis techniques, such as artificial intelligence, neural networks, and signal processing.

The department performs Navy-sponsored research pertaining to broad application of CSM technologies. Prior applications have included ship service gas turbine engines, a helicopter rotor diagnostic system, and a waterjet paint removal system. Pictured on the left is a hand-held interface to the Helicopter Health Information Notification and Tracking System (HHINTS).

Key competencies include:

• Failure Modes and Reliability Analysis
• Traditional and Seeded Fault Testing
• CSM Data Acquisition/Archival
• Machinery Vibration Analysis
• Oil and Debris Analysis
• Power Machinery Modeling
• Thermo/Fluid/Electrochemical Systems Modeling
• Multi-Sensor Data Fusion
• Automated Reasoning
• Distributed Diagnostics

PSU ARL diagnostic and  prognostic systems research has focused on three primary areas: sensing, modeling, and reasoning, as characterized by the Multidisciplinary University Research Initiative for Integrated Predictive Diagnostics (MURI IPD).

The objective of complex systems monitoring is to accurately detect the current state of mechanical systems and accurately predict systems' remaining useful lives. This enables organizations to perform maintenance only when needed to prevent operational deficiencies or failures, essentially eliminating costly periodic maintenance and greatly reducing the likelihood of machinery failures.

CSM uses integrated, multi-sensor systems to detect and diagnose emerging equipment problems and to predict how long the equipment can effectively serve its operational purpose. The systems collect, fuse, and evaluate real-time data using algorithms that correspond the unique signals to their causes (e.g., vibrations created by a developing fault). The system alerts maintenance personnel to the problem, enabling maintenance activities to be scheduled and performed, as needed, before operational effectiveness is compromised. 

CSM represents one of the most promising developments in the evolution of maintenance practices. As organizations are increasingly faced with demands to lower maintenance costs, increase product quality, and hasten organizational responsiveness/operational readiness, CSM has emerged as a viable alternative to traditional planned maintenance, run-to-failure operation, and the various maintenance approaches between those two extremes.