Precedent-Free Fault Isolation in a Diesel Engine Exhaust Gas Recirculation System

Cholette, Michael E.; Djurdjanović, Dragan

doi:10.1115/1.4005511

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…The system-based monitoring paradigm has seen numerous successful applications in the recent years, including monitoring of automotive engine systems (Cholette & Djurdjanovic, 2012), semiconductor manufacturing tools (Bleakie & Djurdjanovic, 2016), and human muscle performance (Musselman, Gates, &Djurdjanovic, 2017 andMadden, Djurdjanovic, &Deshpande, 2021). This methodology's success can be attributed to its ability to capture not only the anomalies in the input and output signals emitted by a system, but also any anomalous relationships between the inputs and outputs.…”

Section: Methodsmentioning

confidence: 99%

“…For more details regarding the GSMMS models, please refer toLiu, Djurdjanovic, Marko, andNi, 2009 and/or Cholette Djurdjanovic, 2012. With the time-series of inputs and outputs of the data-driven model, we can train and track the dynamic models of each muscle based on the data emitted during one's exercise. Training of each muscle model was done using data observed in the initial stages of the exercisewhen the muscles were least fatigued.…”

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confidence: 99%

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System-based Monitoring of Muscular Fatigue in Lower-Extremity Movement

Bertelson,

Molina,

Neptune

et al. 2023

PHM_CONF

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Physical fatigue accounts for many injuries in the workplace, sports arena, or battlefield. The traditional approaches to monitor fatigue rely on detecting and measuring shifts in the person’s muscular surface electromyography (sEMG) signals. However, assessing neuromuscular fatigue based purely on sEMG signals fails to account for the changing muscle dynamics during long dynamic physical tasks. To combat this dilemma, a system-based methodology has been recently developed and applied to several upper-extremity tasks. In this paper, we validate the efficacy of this novel methodology on the lower extremities during a dynamic activity. Specifically, the system-based monitoring methodology was applied to a cycling endurance task. It was statistically demonstrated that the system-based methodology resulted in a more-sensitive and less noisy metric, in comparison with an EMG-based methodology. The efficacy of the methodology was further illustrated by analyzing the inter-segmental recovering and fatiguing trends, which aligned with each muscle’s expected inter-muscle synergistic relationship.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

System-based Monitoring of Muscular Fatigue in Lower-Extremity Movement

Bertelson,

Molina,

Neptune

et al. 2023

PHM_CONF

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“…In a later study (Musselman, Gates, & Djurdjanovic, 2017), the authors analyzed the same dataset as the one used in (Musselman et al, 2016), but with a significant improvement in the sense that the study reported in (Musselman et al, 2017) took into account inherent non-linearities in the dynamics of human body motion by replacing the vARX models from (Musselman et al, 2016) with non-linear dynamic models relating instantaneous EMG intensities and frequencies to the joint angular velocities. Specifically, the study reported in (Musselman et al, 2017) used the so-called Growing Structure Multiple Model System (GSMMS) (Cholette & Djurdjanovic, 2012) models to represent musculoskeletal dynamics. This was motivated by the capability of those "divide-and-conquer" type models 2 to elegantly identify situations in which model inputs significantly differ from those observed during the modelbuilding process.…”

Section: Review Of Prior Research In System Based Approaches To Monit...mentioning

confidence: 99%

“…Therefore, the stationary input assumption is no longer required in the system-based paradigm. Consequently, system-based approaches have seen numerous applications in monitoring of systems that undergo highly dynamic operating regimes, such as automotive engine systems (Cholette & Djurdjanovic, 2012;Liu, Djurdjanovic, Marko, & Ni, 2009), electricity generators (Djurdjanovic, Hearn, & Liu, 2010), robotics (Bryant, 2014;Costuros, 2013) and manufacturing systems (Shi, 2006). Recently, the system-based monitoring paradigm also gained attention in the domain of monitoring of muscular fatigue during human body movements and this paper can be seen as a contribution to the research in that direction.…”

Section: Introductionmentioning

confidence: 99%

Model-based Fusion of Surface Electromyography with Kinematic and Kinetic Measurements for Monitoring of Muscle Fatigue

Gates

Johnson

et al. 2022

IJPHM

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This study proposes a novel method for monitoring muscle fatigue using muscle-specific dynamic models which relate joint time-frequency signatures extracted from the relevant electromyogram (EMG) signals with the corresponding estimated muscle forces. Muscle forces were estimated using physics-driven musculoskeletal models which incorporate muscle lengths and contraction velocities estimated from the available kinematic and kinetic measurements. For any specific individual, such a muscle-specific dynamic model is trained using EMG and movement data collected in the early stages of an exercise, i.e., during the least-fatigued behavior. As the exercise or physical activity of that individual progresses and fatigue develops, residuals yielded by that model when approximating the newly arrived data shift and change because of the fatigue-induced changes in the underlying dynamics. In this paper, we propose quantitative evaluation of those changes via the concept of a muscle-specific Freshness Index (FI) which at any given time expresses overlaps between the distribution of that muscle’s model residuals observed on the most recently collected data and the distribution of modeling residuals observed during non-fatigued behavior. The newly proposed method was evaluated using data collected during a repetitive sawing motion experiment with 12 healthy participants. The performance of the FI as a fatigue metric was compared with the performance of the instantaneous frequency of the relevant EMG signals, which is a more traditional and widely used metric of muscle fatigue. It was found that the FI reflected the progression of muscle fatigue with desirable properties of stronger monotonic trends and smaller noise levels compared to the traditional, instantaneous frequency-based metrics.

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Monitoring of complex systems of interacting dynamic systems

2011

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Increases in functionality, power and intelligence of modern engineered systems led to complex systems with a large number of interconnected dynamic subsystems. In such machines, faults in one subsystem can cascade and affect the behavior of numerous other subsystems. This complicates the traditional fault monitoring procedures because of the need to train models of the faults that the monitoring system needs to detect and recognize. Unavoidable design defects, quality variations and different usage patterns make it infeasible to foresee all possible faults, resulting in limited diagnostic coverage that can only deal with previously anticipated and modeled failures. This leads to missed detections and costly blind swapping of acceptable components because of one's inability to accurately isolate the source of previously unseen anomalies. To circumvent these difficulties, a new paradigm for diagnostic systems is proposed and discussed in this paper. Its feasibility is demonstrated through application examples in automotive engine diagnostics.

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Precedent-Free Fault Isolation in a Diesel Engine Exhaust Gas Recirculation System

Cited by 7 publications

References 20 publications

System-based Monitoring of Muscular Fatigue in Lower-Extremity Movement

System-based Monitoring of Muscular Fatigue in Lower-Extremity Movement

Model-based Fusion of Surface Electromyography with Kinematic and Kinetic Measurements for Monitoring of Muscle Fatigue

Monitoring of complex systems of interacting dynamic systems

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