| dc.description.abstract | Current methodologies on health assessment and diagnostics of systems and components are limited in analysis and accuracy. Methodologies like artificial intelligence or limit checks examine statistical significance rather than the actual degradation occurring in the system’s components. The authors have demonstrated two approaches based on information theory and thermodynamics to analyze the degradation dynamics and their significance in faults. In previous publications, the authors have established the similarity between a machine and Shannon’s communication channel and the relation between machine degradation and channel capacity [the amount of information that can be transmitted through a given channel]. The authors call this equivalency “Machine Capacity,” and it relates to the machine availability to perform the desired work with enough quality and confidence under time and information constraints. Different degradation mechanisms increase Entropy (which decreases Information Entropy used for channel capacity), in particular “modes'' that might be detectable under the right conditions and with appropriate sensors. Experimental results have shown that a quick health screening and assessment can be conducted with limited historical data, and can yield definite fault isolation with enough historical data. Additionally, due to its simple implementation, this methodology can be done online with simple computing units. Degradation of systems –like motor-pumps, compressors, or fans– induce thermodynamic changes. Those changes, related to degradation mechanisms such as friction, fracture, heat transfer, plastic deformation, among others, generate entropy. The methodology in this work quantifies the entropy generated by degradation in a system, correlates this entropy with the rate of specific degradation mechanisms, and shows how it affects particular variables of interest. These methods resulted in the Degradation-Entropy Generation (DEG) Theorem that successfully assessed battery, grease, material fatigue, and motor degradation. DEG models show an impressive near 100% correlation with measured data. Via deviations from baseline values and profiles obtained from a healthy motor pump, this article shows how DEG elements consistently detect faults in the pump, including shaft imbalance, soft foot, and misalignment. The experimental approaches have demonstrated that this method can be used for failure analysis and fault detection on a variety (any) systems, including pumps, compressors, dry gas seals, valves, fuel cells, etc. This study compares and combines these two methods to understand how they can be used as tools to assess machine health and availability. The process and control data such as voltages, currents, speeds, pressures, temperatures, and flow rates were acquired in several experiments. | |
