Publications
Explore the research behind Machine Essence’s approach to machine functional health, degradation and reliability.
The work connects degradation physics, machine capacity and reliability decisions: how equipment loses capability, how that change can be detected, and how the findings support maintenance planning.
On the thermodynamics of degradation
2008 · Journal article
M. D. Bryant; M. M. Khonsari; F. F. Ling
Proceedings of the Royal Society A, 464(2096), 2001–2014
Research focus: The physical connection between irreversible degradation and entropy generation, providing the foundation for DEG-based degradation models.
Original abstract The science base that underlies modelling and analysis of machine reliability has remained substantially unchanged for decades. Therefore, it is not surprising that a significant gap exists between available machinery technology and science to capture degradation dynamics for prediction of failure. Further, there is a lack of a systematic technique for the development of accelerated failure testing of machinery components. This article develops a thermodynamic characterization of degradation dynamics, which employs entropy, a measure of thermodynamic disorder, as the fundamental measure of degradation; this relates entropy generation to irreversible degradation and shows that components of material degradation can be related to the production of corresponding thermodynamic entropy by the irreversible dissipative processes that characterize the degradation. A theorem that relates entropy generation to irreversible degradation, via generalized thermodynamic forces and degradation forces, is constructed. This theorem provides the basis of a structured method for formulating degradation models consistent with the laws of thermodynamics. Applications of the theorem to problems involving sliding wear and fretting wear, caused by effects of friction and associated with tribological components, are presented.
Modeling Degradation Using Thermodynamic Entropy
2014 · Conference paper
Michael D. Bryant
Annual Conference of the PHM Society, 6(1)
Research focus: Applying the DEG framework to models of wear, fatigue and battery degradation, and using those models to examine degradation progression and prognosis.
Original abstract Manufacture transforms raw materials into finished components. Ageing and degradation of components, driven by dissipative processes, irreversibly alter material structures. The second and third laws of thermodynamics assert that these dissipative processes must generate entropy. This entropy is a fundamental quantity to describe ageing and degradation. This recognition led to a Thermodynamic Degradation Paradigm encapsulated in a Degradation Entropy Generation (DEG) Theorem, wherein the rate of degradation was related to the irreversible entropies produced by the underlying dissipative physical processes that age and degrade components. This paradigm and theorem permit a structured approach to modeling degradation of any kind. If properly applied, the DEG Theorem leads to a differential equation in a variable that describes the degradation. The equation depends on the operational and environmental variables that characterize the system. Integration of the equation accumulates the degradation over time. This approach has led to accurate models for progression of and failure by wear, fatigue, and battery degradation that are consistent with prior models. This article will review the Thermodynamic Degradation Paradigm and Degradation Entropy Generation Theorem, and apply these to formulate predictive models of wear, fatigue, and battery degradation, i.e., differential equations that govern the degradation or ageing. The article will conclude with a discussion on how to use these governing degradation equations for machine prognosis. Michael D. Bryant, Annual Conference of the PHM Society (2014). CC BY 3.0 US.
Shannon’s Machine Capacity & Degradation-Entropy Generation Methodologies for Failure Detection: Practical Applications to Motor-Pump Systems
2021 · Conference paper
Juan S. Rincon; Michael D. Bryant; Jude Osara; Benito R. Fernández
Turbomachinery & Pump Symposia, Texas A&M Engineering Experiment Station
Research focus: Applying machine-capacity and DEG methods to motor-pump measurements, connecting changes from healthy reference behavior with fault detection and functional health.
Original 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 in particular “modes'' that might be detectable under the right conditions and with appropriate sensors. Experimental results have shown that a quick health assessment can be concluded with limited historical data, and it can yield definite fault isolation with enough historical data. Additionally, due to its simple implementation, this methodology can be done online on simple computing units. All systems –like motor-pumps, compressors, or fans– undergo thermodynamic changes. Those changes, related to degradation mechanisms such as friction, fracture, heat transfer, plastic deformation, among others, generate entropy. The methodology used in this work quantifies the entropy generated by degradation in a system, and correlates it (entropy generated) with the rate of specific degradation mechanisms, and how it affects particular variables of interest. The formulation of these methodologies has resulted in the Degradation-Entropy Generation (DEG) Theorem that has successfully been applied to 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 paper 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 to realize 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 run in conjunction with Mechanical Solutions Inc. © 2021 Turbomachinery Laboratory, Texas A&M Engineering Experiment Station. Research results are reported in their original context.
Emerging Approaches in Equipment Reliability Management: Bridging Technology and Business Performance
2025 · Conference presentation and abstract
David Lim; Antonio Machado; Benito Fernandez; Michael D. Bryant; Daniel Machado
IMVIC 2025, Kuala Lumpur, May 2025; AMVIP
Research focus: Connecting equipment-health information with Reliability Centered Maintenance, asset criticality and spare-parts planning so engineering findings inform business decisions.
Original conference abstract Abstract Traditional reliability metrics such as Mean Time Between Failure (MTBF) and Overall Equipment Effectiveness (OEE) offer undoubtful useful insights into plant performance but often fail in real-life to capture specific equipment failures or system degradation. With current advancements in sensor technologies, data analytics, and AI, this paper presents a fresh and progressive perspective on Reliability-Centered Maintenance (RCM). The methodology integrates predictive diagnostics and prognostics within live Process Engineering Flow Schemes (PEFS), creating a dynamic, real-time view of equipment health and system reliability. By synchronizing predictive maintenance with spare parts management, this approach shows its usefulness in reducing unplanned downtimes, optimizing resources, and aligns maintenance strategies with business goals. Objectives/Scope To propose an evolved RCM framework that leverages modern data collection technologies and predictive analytics, integrated with Risk-Based Inspection (RBI) methodology from the static equipment realm. The goal is to move beyond traditional statistical averages to actionable, system-specific insights that improve reliability, safety, and overall business performance. Methods, Procedures, Process The enhanced methodology involves advanced diagnostics using AI-assisted tools to observe and identify failure modes and predict potential breakdowns. Equipment are categorized by criticality, and diagnostic systems are fine-tuned to ensure actionable Observability. By overlaying live-reliability data onto PEFS, equipment health and projected performance are made actionable to all engineering disciplines, enabling collaborative and informed decision-making. Results, Observations, Conclusions This approach transforms maintenance strategies by shifting from the traditional predictive maintenance approach to a fully integrated predictive and proactive interventions within all business functions. The methodology based on experience demonstrates how precise failure predictions minimize downtime and costs while boosting production reliability. Thus, integrating failure prognosis and predictions with inventory management ensures optimized availability of necessary parts, further reducing disruptions. Novel/Additive Information This methodology introduces the concept of "Real-Life Reliability," where AI-driven tools and sensors provide clear, specific, actionable insight into equipment health, leading to precise execution. It emphasizes proactive maintenance planning and collaboration between operations, supply chain, and OEMs, presenting a transformative shift in maintenance strategies.