Condition monitoring

About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.

A general prognostic tracking algorithm for predictive maintenance

Abstract: Prognostic health management (PHIM) is a technology that uses objective measurements of condition and failure hazard to adaptively optimize a combination of availability, reliability, and total cost of ownership of a particular asset. Prognostic utility for the signature features are determined by transitional failure experiments. Such experiments provide evidence for the failure alert threshold and of the likely advance warning one can expect by tracking the feature(s) continuously. Kalman filters are used to track changes in features like vibration levels, mode frequencies, or other waveform signature features. This information is then functionally associated with load conditions using fuzzy logic and expert human knowledge of the physics and the underlying mechanical systems. Herein is the greatest challenge to engineering. However, it is straightforward to track the progress of relevant features over time using techniques such as Kalman filtering. Using the predicted states, one can then estimate the future failure hazard, probability of survival, and remaining useful life in an automated and objective methodology.

Mechatronic Systems Design for an Autonomous Robotic System for High-Efficiency Bridge Deck Inspection and Evaluation

Abstract: The condition of bridges is critical for the safety of the traveling public. Bridges deteriorate with time as a result of material aging, excessive loading, environmental effects, and inadequate maintenance. The current practice of nondestructive evaluation (NDE) of bridge decks cannot meet the increasing demands for highly efficient, cost-effective, and safety-guaranteed inspection and evaluation. In this paper, a mechatronic systems design for an autonomous robotic system for highly efficient bridge deck inspection and evaluation is presented. An autonomous holonomic mobile robot is used as a platform to carry various NDE sensing systems for simultaneous and fast data collection. The robot's NDE sensor suite includes ground penetrating radar arrays, acoustic/seismic arrays, electrical resistivity sensors, and video cameras. Besides the NDE sensors, the robot is also equipped with various onboard navigation sensors such as global positioning system (GPS), inertial measurement units (IMU), laser scanner, etc. An integration scheme is presented to fuse the measurements from the GPS, the IMU and the wheel encoders for high-accuracy robot localization. The performance of the robotic NDE system development is demonstrated through extensive testing experiments and field deployments.

Expert system development for vibration analysis in machine condition monitoring

Abstract: Expert systems can be adapted for machine condition monitoring data interpretation due to the ability to identify systematic reasoning processes As vibration analysis in condition monitoring is still generally performed by highly trained professionals, the use of expert systems would allow a greater analysis throughput as well as enabling technicians to perform routine analysis The development of an expert system for vibration analysis of fixed plant is discussed, as well as laboratory and industry testing Unique to existing developments, the expert system incorporates triaxial and demodulated frequency and time domain vibration data analysis algorithms for high accuracy fault detection The tests confirm the potential value of the expert system for both laboratory and on-site maintenance departments of large manufacturing and mineral processing plants