Multi-Branch Hidden Semi-Markov Modeling for RUL Prognosis

International audienceDeterioration modeling and remaining useful life (RUL) estimation of equipment are key enabling tasks for the implementation of a predictive maintenance (PM) policy, which plays nowadays an important role for maintaining engineering systems. Hidden Markov Models (HMM) have been used as an efficient tool for modeling the deterioration mechanisms as well as for estimating the RUL of monitored equipment. However, due to some assumptions not always justified in practice, the applications of HMM on real-life problems are still very limited. To tackle this issue and to relax some of these unrealistic assumptions, this paper proposes a multi-branch Hidden semi-Markov modeling (MB-HSMM) framework. The proposed deterioration model comprises several different branches, each one being itself an HSMM. The proposed model offers thus the capacity to 1) explicitly model the sojourn time in the different states and 2) take into account multiple co-existing and competing deterioration modes, even within a single component. A diagnosis and RUL prognosis methodology based on the MB-HSMM model is also proposed. Thanks to its multiple branches property, the MB-HSMM model makes it possible not only to assess the current health status of the component but also to detect the actual deterioration mechanism. Based on the diagnostic results, the component RUL can then be calculated. The performance of the proposed model and prognosis method is evaluated through a numerical study. A Fatigue Crack Growth (FCG) model based on the Paris-Erdogan law is used to simulate deterioration data of a bearing under different operation conditions. The results show that the proposed MB-HSMM gives a very promising performance in deterioration mode detection as well as in the RUL estimation, especially in the case where these deterioration modes exhibit very different dynamics

Hidden Markov Models for diagnostics and prognostics of systems under multiple deterioration modes

International audienceMulti-state systems have recently attracted a great deal of interest with regards to reliability and maintenance. Since most mechanical equipment operates under some sorts of stress or load, it tends to degrade over time, thus possibly resulting in discrete degradation states (damage degrees), ranging from perfect functioning to complete failure. Over recent years, Hidden Markov Models (HMMs) have been applied to model these discrete degradation states for diagnostic and prognostic purposes. However, most of the reported researches on HMMs for multi-state equipment in the literature consider only one degradation mechanism of degradation processes. The present paper proposes a novel model called multi-branch HMM (MB-HMM) to deal with deterioration processes modeling under multiple competing modes. To illustrate the proposed approach, a numerical study is given

Hidden Markov models for failure diagnostic and prognostic.

International audienceThis paper deals with an estimation of the Remaining Useful Life of bearings based on the utilization of Mixture of Gaussians Hidden Markov Models (MoG-HMMs). The raw signals provided by the sensors are first processed to extract features, which permit to model the physical component and its degradation. The prognostic process is done in two phases: a learning phase and an evaluation phase. During the first phase, the sensors' data are processed in order to extract appropriate and useful features, which are then used as inputs of dedicated learning algorithms in order to estimate the parameters of a MoG-HMM. The obtained model represents the behavior of the component including its degradation. In addition, the model contains the number of health states and the stay durations in each state. Once the learning phase is done, the generated model is exploited during the second phase, where the extracted features are continuously injected to the learned model to assess the current health state of the physical component and to estimate its remaining useful life and the associated confidence. The proposed method is tested on a benchmark data taken from the "NASA prognostic data repository" related to bearings used under several operating conditions. Moreover, the developed method is compared to two methods: the first using traditional HMMs with exponential time durations and the second using regular Hidden Semi Markov Model (HSMM). Finally, simulation results are given and discussed at the end of the paper

Hidden semi-Markov Models for Predictive Maintenance of Rotating Elements

The dissertation at hand introduces a novel algorithm to predict the remaining useful life (RUL) of rotating components such as grooved ball bearings or gear boxes. The focus of the implemented method is on so-called Hidden semi-Markov Models (HsMM), which are suitable for the modeling of sequential events. The selected method is designed to support maintenance processes based on advisory generation in the context of predictive maintenance. In this regard, the goal of predictive maintenance is the generation of predictions about the RUL of examined components based on their current state. Thus, maintenance events can be scheduled more precisely, downtime is reduced, and the actual useful life of components can be exploited. After an initial classification of the proposed method in the context of Prognostics and Health Management (PHM), the concept is described. The algorithm is based on methods, which apply historical data of the component’s degradation process for the estimation of the current and future damage state. A novel concept in the field of HsMM permits the identification of similar damage states within different degradation datasets to obtain more information about the damage process of the examined components. A further research question analyzes, whether the consideration of available information about the component’s endured load increases the prognostic performance. First results in the context of verification conclude the concept description. Subsequently, the design and realization of a new test rig, which permits an accelerated bearing aging for induction machines due to a so-called bearing current, is presented. Here, an alternating current flows through the tested bearing and reduces its life cycle significantly. By means of these data, the concept is evaluated. In comparison to state-of-the-art methods in field of bearing PHM, the validation is executed by examining the motor current of the induction machine instead of the widespread analysis of the resulting vibration signal. The results indicate that the precise and accurate prediction of the bearing’s RUL is possible. In addition, the consideration of already endured load for the generation of life cycle predictions is beneficial. A selected state-of-the-art algorithm, also based on HsMM, permits a realistic evaluation of the achieved prognostic performance. The dissertation ends with the estimation of possible cost savings in an exemplary aircraft maintenance scenario. For this, the obtained prognostic results are assessed with a state-of-the-art cost-benefit analysis tool. The outcome indicates that the application of the proposed algorithm leads to savings due to e.g. decreased downtimes

Exploring the impact of osteoporosis on myogenesis

Title from PDF of title page, viewed on June 29, 2015Dissertation advisor: Marco BrottoVitaIncludes bibliographic references (pages 301-329)Thesis (Ph.D.)--School of Nursing and Health Studiies. University of Missouri--Kansas City, 2015Aging is accompanied by a significant decline in bone mass and strength (osteoporosis) and in muscle mass and strength (sarcopenia). These conditions pose a tremendous threat as each year, one in three older adults living in the community falls. Muscle weakness is a primary risk factor for falls and the associated morbidity and mortality, especially among older adults with osteoporosis. Nurses are aware of the risks and are often in a position to effect a change. For this reason, nurses are positioned to be involved in and to direct research aimed at better understanding these conditions and to make discoveries with translational impact. Until recently, bones and muscles were viewed to function in a mechanical partnership. Emerging research, however, demonstrates a much more complex relationship, resulting not only from mechanical forces, but also from an exchange of biochemical factors. The purpose of this in vitro controlled trial was to explore this biochemical exchange, and investigate the impact of bone factors on skeletal muscle cell differentiation (myogenesis) in the presence of osteoporosis. A series of studies have been completed in mouse models, and our concomitant goal was to expand these studies into humans. Serum used was collected from research subjects in an ongoing case-control study designed to characterize defects in bone quality that contribute to low trauma fractures in postmenopausal women. Using a combination of biophysical, biochemical, and physiological approaches, the serum from subjects with (CASE) and without (CNTRL) osteoporosis was applied to human skeletal muscle cells. The extent of myogenesis in each group was assessed through immunostaining for visualization and calculation of fusion index (i.e., the myogenesis index), flow cytometry for cell cycle analysis, and intracellular calcium measurements for data related to cellular function. Findings from this study will contribute to the growing body of knowledge related to the biochemical communication between bones and muscles, bone-muscle crosstalk. In addition, this study illustrates an excellent opportunity for basic scientists and clinicians to work together to decrease the devastating impact of sarcopenia and osteoporosis.Introduction -- Review of literature -- Theoretical framework and methodology -- Results -- Discussion -- Appendix A. IRB Authorization Agreement between UMKC and Creighton University Osteoporosis Research Center -- Appendix B. List of Identified Factors: Exploring the biochemical communication between bones, muscles, and other body tissues -- Appendix C. Human Skeletal Muscle Cells (HSMM) Protocols -- Appendix D. Protocol: Protocol, HSMM, Immunostaining for Fusion Index Calculations -- Appendix E. Protocol: HSMM, Calcium Imaging -- Appendix F. Protocol: HSMM, Flow Cytometry, MUSE™ Cell Cycle Assay -- Appendix G. Data Collected: HSMM, Immunostaining for Fusion Index Calculations -- Appendix H. Data Collected: HSMM, Calcium Imaging -- Appendix I. Data Collected: HSMM, Flow Cytometry for MUSE ™ Cell Cycle Assay -- Appendix J. Comprehensive Tables, Data Collecte

End-to-end anomaly detection in stream data

Nowadays, huge volumes of data are generated with increasing velocity through various systems, applications, and activities. This increases the demand for stream and time series analysis to react to changing conditions in real-time for enhanced efficiency and quality of service delivery as well as upgraded safety and security in private and public sectors. Despite its very rich history, time series anomaly detection is still one of the vital topics in machine learning research and is receiving increasing attention. Identifying hidden patterns and selecting an appropriate model that fits the observed data well and also carries over to unobserved data is not a trivial task. Due to the increasing diversity of data sources and associated stochastic processes, this pivotal data analysis topic is loaded with various challenges like complex latent patterns, concept drift, and overfitting that may mislead the model and cause a high false alarm rate. Handling these challenges leads the advanced anomaly detection methods to develop sophisticated decision logic, which turns them into mysterious and inexplicable black-boxes. Contrary to this trend, end-users expect transparency and verifiability to trust a model and the outcomes it produces. Also, pointing the users to the most anomalous/malicious areas of time series and causal features could save them time, energy, and money. For the mentioned reasons, this thesis is addressing the crucial challenges in an end-to-end pipeline of stream-based anomaly detection through the three essential phases of behavior prediction, inference, and interpretation. The first step is focused on devising a time series model that leads to high average accuracy as well as small error deviation. On this basis, we propose higher-quality anomaly detection and scoring techniques that utilize the related contexts to reclassify the observations and post-pruning the unjustified events. Last but not least, we make the predictive process transparent and verifiable by providing meaningful reasoning behind its generated results based on the understandable concepts by a human. The provided insight can pinpoint the anomalous regions of time series and explain why the current status of a system has been flagged as anomalous. Stream-based anomaly detection research is a principal area of innovation to support our economy, security, and even the safety and health of societies worldwide. We believe our proposed analysis techniques can contribute to building a situational awareness platform and open new perspectives in a variety of domains like cybersecurity, and health

Hidden Markov Model-based Methods In Condition Monitoring of Machinery Systems

Ph.DDOCTOR OF PHILOSOPH

On condition-based maintenance for machine components

The goal of condition-based maintenance (CBM) is to base the decisions whether or not to perform maintenance on information collected from the machine or component of interest. A condition-based maintenance tool should be able to diagnose if the component of interest is in a state of failure but the ultimate goal of a CBM tool is to be able to estimate time until failure, either in terms of remaining useful life (RUL) or estimated time to failure (ETTF). Therefore a CBM tool should have both diagnostic and prognostic features. This master’s thesis was carried out at a company within the packaging industry and the goal was to implement a CBM tool with the possibility to estimate RUL for a set of critical components which could serve as a base for further development within the company. The selection of components to focus on was part of the thesis as well. The process of implementing CBM with prognostic functionality was more difficult than expected and the goal of estimating RUL was not met for any of the components, but the work that has been done forms a basis for further development. Thus, this thesis will serve as a pre-study on developing CBM and contains information of what is required in order to be successful

Investigating the influence of inflammatory mediators on non-inflammatory features of sporadic inclusion body myositis in vitro

Sporadic inclusion body myositis (sIBM) is a progressive muscle disease causing weakness and ambulation difficulty. Muscle of sIBM patients presents with inflammation and degeneration. CD8+ T cells infiltrate affected muscles and inflammatory cytokines are upregulated. Degenerative/non-inflammatory features are observed including sarcoplasmic accumulation of proteins such as TDP-43 and p62, and TDP-43 sarcoplasmic mislocalisation. The cause of sIBM symptoms is poorly understood and there are no effective treatments. Further understanding of the interaction between inflammatory and non inflammatory features of sIBM may elucidate potential treatment targets. This thesis aimed to explore the effect of inflammation on non-inflammatory features of sIBM in healthy human myotubes. The effects of IL-1β and IFNγ, conditioned medium or coculture with a cytotoxic immune cell line TALL-104 on p62 and TDP-43 sarcoplasmic aggregation, protein expression, and TDP-43 subcellular localisation was investigated. Using 3D myotube cultures termed myobundles, the effect of inflammatory conditions on force generation was examined. No treatment caused aggregation of TDP-43, suggesting these inflammatory factors do not trigger TDP-43 sarcoplasmic aggregation in these cells. IL-1β+IFNγ combined but not these cytokines separately caused increased size of p62 puncta, but this may represent increased autophagic flux instead of dysfunctional p62 aggregation. Active force from myobundles representing muscle strength was not affected by IL-1β+IFNγ or TALL-104 coculture after 48 hours incubation. IL-1β+IFNγ increased half relaxation time and time to peak force, suggesting fatigue induction. This indicates acute exposure to inflammatory cytokines or cytotoxic immune cells may not trigger muscle weakness. Overall, these results highlight TDP-43 aggregation may not be influenced by inflammatory factors, but alterations in p62 can occur with simultaneous multiple inflammatory insults in cultured muscle cells. This work suggests further investigations of myobundle cultures with sIBM-like inflammatory mediators may be warranted to investigate muscle weakness