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  • 1
    Online Resource
    Online Resource
    Deep Learning Approach for Operational Transfer Path Analysis: Case Study of Electric Vehicles
    PHM Society ; 2023
    In:  PHM Society Asia-Pacific Conference Vol. 4, No. 1 ( 2023-09-12)
    Details
    In: PHM Society Asia-Pacific Conference, PHM Society, Vol. 4, No. 1 ( 2023-09-12)
    Abstract: This paper presents a new approach to fault diagnosis of the drivetrains of the electric vehicle. Most commercially available electric vehicles do not have accelerometers on electric drivetrains making it difficult to detect fault characteristics of the drivetrains of the electric vehicle, whereas accelerometers exist on the driver's seat. The proposed approach’s key idea is based on the operational transfer path analysis that determines the transfer function between the source and receiver. The transfer function is derived by training a deep learning model. The deep learning model converts the driver's seat vibration signals into drivetrains vibration signals. The validity of the proposed approach is evaluated using data from the durability test of real electric vehicles. It is anticipated that the proposed approach is effective to diagnose electric vehicle drivetrains subjected to external noise conditions.
    Type of Medium: Online Resource
    ISSN: 2994-7219 , 2994-7219
    URL: Article
    DOI: 10.36001/phmap.2023.v4i1.3793
    Language: Unknown
    Publisher: PHM Society
    Publication Date: 2023
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  • 2
    Online Resource
    Online Resource
    Fault Diagnosis of Multiple Components in Complex Mechanical System Using Remote Sensor
    PHM Society ; 2024
    In:  PHM Society European Conference Vol. 8, No. 1 ( 2024-06-27), p. 9-
    Details
    In: PHM Society European Conference, PHM Society, Vol. 8, No. 1 ( 2024-06-27), p. 9-
    Abstract: This study proposes an approach to monitor multiple components in complex mechanical systems using a single, externally placed remote sensor. In automobiles and petrochemical plants, where numerous components (e.g., powertrain, bearing, and gear), sensor placement is often compromised by cost and installation environment constraints, resulting in sensing the components far from the regions of interest. To address this challenge, this paper proposes an Operational Transfer Path Analysis (OTPA)-based approach that derives the transfer functions between the vibration excitation source and the measurement point (i.e., receiver). The model for OTPA enables the reverse estimation of the excitation source’s signal from the receiver. Subsequently, the estimated (i.e., synthesized) source signal is fed into a diagnostic model to identify system faults. The OTPA and diagnostic models are constructed using neural network architectures, enabling better adaptation to operational conditions and system-induced nonlinearities. The proposed approach is validated from case studies using hydraulic piston pumps in construction vehicles and next-generation electric vehicles.
    Type of Medium: Online Resource
    ISSN: 2325-016X , 2325-016X
    URL: Article
    DOI: 10.36001/phme.2024.v8i1.3999
    Language: Unknown
    Publisher: PHM Society
    Publication Date: 2024
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  • 3
    Online Resource
    Online Resource
    Coal Pulverizer Prognostics Data Challenge in PHMAP 2017 and Suggestions for Future Studies
    PHM Society ; 2020
    In:  International Journal of Prognostics and Health Management Vol. 9, No. 3 ( 2020-11-20)
    Details
    In: International Journal of Prognostics and Health Management, PHM Society, Vol. 9, No. 3 ( 2020-11-20)
    Abstract: Pulverizers in a power plant are used to grind coal into the form of a fine powder for combustion in a power plant. To secure reliable operation, redundant pulverizers should be installed in power plants and monitored. Pulverizers can be operated and maintained in a cost-effective manner by correctly estimating the current health condition and remaining useful life of the pulverizer’s gearbox system. To this end, the Data Challenge Committee of the PHM Asian Pacific 2017 (PHMAP 2017) conference organized an open competition on the topic of coal pulverizer health estimation based on a real working power station. This paper presents the original problem and given facts, as well as the list of winners of the Data Challenge Competition. We anticipate that this paper can be used as a reference in the development of a prognostic method that can accurately predict the health conditions of coal pulverizers.
    Type of Medium: Online Resource
    ISSN: 2153-2648 , 2153-2648
    URL: Article
    DOI: 10.36001/ijphm.2018.v9i3.2744
    Language: Unknown
    Publisher: PHM Society
    Publication Date: 2020
    detail.hit.zdb_id: 2675345-5
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  • 4
    Online Resource
    Online Resource
    Risk Prediction of Engineering Assets: An Ensemble of Part Lifespan Calculation and Usage Classification Methods
    PHM Society ; 2020
    In:  International Journal of Prognostics and Health Management Vol. 5, No. 2 ( 2020-11-01)
    Details
    In: International Journal of Prognostics and Health Management, PHM Society, Vol. 5, No. 2 ( 2020-11-01)
    Abstract: For the 2014 Prognostics and Health Management (PHM) Data Challenge Competition, the PHM Society proposed a problem surrounding risk prediction of engineering assets. We worked to address this problem by statistically analyzing the maintenance records, extracting key data features, and proposing an ensemble method for accurate prediction of imminent failure of assets. The data analysis of maintenance records provided two key pieces of information: 1) parts and part replacement reasons were able to be classified into corrective and scheduled maintenance actions, and 2) a linear relation was found between failure frequency and usage time. Based on this information, we proposed two risk-prediction methods, namely, a method based on part lifespan calculation and a method based on usage classification. Further work showed that the ensemble approach, which combined these two methods with a risk assignment formulation, provided more accurate risk prediction. The score predicted by the ensemble approach ranked in the second place in the 2014 PHM Data Challenge Competition.
    Type of Medium: Online Resource
    ISSN: 2153-2648 , 2153-2648
    URL: Article
    DOI: 10.36001/ijphm.2014.v5i2.2235
    Language: Unknown
    Publisher: PHM Society
    Publication Date: 2020
    detail.hit.zdb_id: 2675345-5
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  • 5
    Online Resource
    Online Resource
    Vibration-based Fault Diagnostics of Planetary Gearbox Using Time Synchronous Averaging with Multiple Window Functions
    PHM Society ; 2015
    In:  Annual Conference of the PHM Society Vol. 7, No. 1 ( 2015-10-18)
    Details
    In: Annual Conference of the PHM Society, PHM Society, Vol. 7, No. 1 ( 2015-10-18)
    Abstract: Recently, numerous number of studies have been made to advance TSA for vibration-based diagnostics of planet gears of a planetary gearbox. To increase signal-to-noise ratio of the vibration signals, various narrow-range window functions centered on sensor’s position have been developed to capture the instances when the planet gears are adjacent to the sensor. However, TSA with such narrow-range window functions is unable to detect the abnormal signal if it is amplified at outside of the sensor’s position due to an unexpected vibration modulation characteristics of the gearbox. This paper proposes a TSA which is robust toward the unexpected vibration modulation characteristics of the gearbox. Multiple narrow-range window functions were employed to perform multiple TSAs rather than employing the sole window function centered on the sensor’s position. Condition indicators with regard to every ring gear’s teeth were derived, and accumulated for the purpose of condition monitoring. Then, optimal position of the window function was determined to maximize capability to detect signals from the faulty gear. For demonstration of the proposed TSA, test was performed with a 2kW testbed having one-stage planetary gearbox within which a planet gear with artificial fault was assembled.
    Type of Medium: Online Resource
    ISSN: 2325-0178 , 2325-0178
    URL: Article
    DOI: 10.36001/phmconf.2015.v7i1.2761
    Language: Unknown
    Publisher: PHM Society
    Publication Date: 2015
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  • 6
    Online Resource
    Online Resource
    Unsupervised Fault Diagnosis of Journal Bearing Rotor System with Heterogeneous Data
    PHM Society ; 2017
    In:  PHM Society Asia-Pacific Conference Vol. 1, No. 1 ( 2017-07-14)
    Details
    In: PHM Society Asia-Pacific Conference, PHM Society, Vol. 1, No. 1 ( 2017-07-14)
    Abstract: In general, it is extremely difficult to obtain failure data of real systems in the field such as power plant rotor systems. To accommodate the dearth of field failure data, conventional approaches employ data collected from a testbed that emulates the normal and faulty conditions of the real systems. Nevertheless, it is obvious that approaches developed with failure data solely collected from a testbed may not be ideal to diagnose the real systems. To this end, this paper proposes a unsupervised fault diagnostic approach for journal bearing systems that incorporates heterogeneous data from the testbed and real field systems. To demonstrate the validity of the proposed approach, a case study is conducted with the RK4 rotor kit and the power plant journal bearing system. The combination of vibration image generation with deep learning helps us use data from systems with an identical working principle but different scales. We anticipate that, by incorporating the heterogeneous data, the proposed approach can diagnose the conditions of actual journal bearing systems in the field more accurately.
    Type of Medium: Online Resource
    ISSN: 2994-7219 , 2994-7219
    URL: Article
    DOI: 10.36001/phmap.2017.v1i1.1772
    Language: Unknown
    Publisher: PHM Society
    Publication Date: 2017
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    Online Resource
  • 7
    Online Resource
    Online Resource
    Describing Problem for the PHMAP 2017 Data Challenge Competition and Suggestions for Future Study
    PHM Society ; 2017
    In:  PHM Society Asia-Pacific Conference Vol. 1, No. 1 ( 2017-07-14)
    Details
    In: PHM Society Asia-Pacific Conference, PHM Society, Vol. 1, No. 1 ( 2017-07-14)
    Abstract: Pulverizers in a power plant are used to grind coal in the form of find power for combustion. To secure reliable operation, redundant pulverizers are installed in the power plant. If the current health condition and remaining useful life of a gearbox system in each pulverizer are estimated correctly, the pulverizers can be operated and maintained in a cost-effective manner. To this end, a Data Challenge Committee in the PHMAP 2017 conference is organized to run an open competition. This paper presents the original problem and observed facts as well as the list of winners of the Data Challenge Competition. We anticipate that this paper can be used as a reference in the development of a prognostic method that can predict the health conditions of the pulverizers accurately.
    Type of Medium: Online Resource
    ISSN: 2994-7219 , 2994-7219
    URL: Article
    DOI: 10.36001/phmap.2017.v1i1.1682
    Language: Unknown
    Publisher: PHM Society
    Publication Date: 2017
    Location Call Number Limitation Availability
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    Online Resource
  • 8
    Online Resource
    Online Resource
    A Solenoid Valve Multi-physics Model for Railway Braking System Diagnosis: Development and Validation
    PHM Society ; 2017
    In:  PHM Society Asia-Pacific Conference Vol. 1, No. 1 ( 2017-07-14)
    Details
    In: PHM Society Asia-Pacific Conference, PHM Society, Vol. 1, No. 1 ( 2017-07-14)
    Abstract: Solenoid valves are an electromagnetically-operated valve that can be used to control the movement of air in railway braking systems. To develop a physics-based diagnostic model for fault detection, it is critical to build a valid model that describes the behavior of solenoid valves accurately. This study presents an experimentally-validated multiphysics analytical model to predict solenoid valve behaviors. The activation of solenoid valves is associated with multi-physics mechanisms that are coupled together. Electromagnetic, fluidic, and mechanical mechanisms are modeled with ordinary differential equations. The results from the multi-physics analytical models are validated with those from the experiments. The undefined parameters of the model are statistically calibrated. The proposed model can be promising for solenoid valve diagnostics in two aspects. First, thecomputational cost of the analytical model is extremely low in comparison with that of a commercial finite element method (FEM). Second, the model provides an insight regarding explicit relationship between the inputs and outputs of solenoid valves.
    Type of Medium: Online Resource
    ISSN: 2994-7219 , 2994-7219
    URL: Article
    DOI: 10.36001/phmap.2017.v1i1.2047
    Language: Unknown
    Publisher: PHM Society
    Publication Date: 2017
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  • 9
    Online Resource
    Online Resource
    TDR-based Multiple Leak Detection System using an S-parameter Transmission Line Model for Long-Distance Pipelines
    PHM Society ; 2020
    In:  International Journal of Prognostics and Health Management Vol. 9, No. 3 ( 2020-11-20)
    Details
    In: International Journal of Prognostics and Health Management, PHM Society, Vol. 9, No. 3 ( 2020-11-20)
    Abstract: Leaks in water distribution systems should be detected to avoid economic, environmental, and social problems. Existing Bayesian Inference based time-domainreflectometry (TDR) methods for leak detection have a limitation for real applications due to the lengthy time in building sample data. As the pipeline distance becomes longer and multiple leaks must be considered in long distance pipelines, the computational time for building training data gets larger. This paper proposes a scattering-parameter-based forward model to relieve computational burden of the existing TDR methods. It was shown that the proposed model outperformed the existing RLGC-based forward model in terms of computational time. The proposed model that is combined with Bayesian inference and TDR signal modeling is validated with an experimental pipeline, leak detectors, transmission line, and TDR instrument for leak detection. In summary, the proposed method is promising for leak detection in long pipelines as well as multiple leaks.
    Type of Medium: Online Resource
    ISSN: 2153-2648 , 2153-2648
    URL: Article
    DOI: 10.36001/ijphm.2018.v9i3.2742
    Language: Unknown
    Publisher: PHM Society
    Publication Date: 2020
    detail.hit.zdb_id: 2675345-5
    Location Call Number Limitation Availability
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    Online Resource
  • 10
    Online Resource
    Online Resource
    Smart Diagnosis of Journal Bearing Rotor Systems: Unsupervised Feature Extraction Scheme by Deep Learning
    PHM Society ; 2016
    In:  Annual Conference of the PHM Society Vol. 8, No. 1 ( 2016-10-03)
    Details
    In: Annual Conference of the PHM Society, PHM Society, Vol. 8, No. 1 ( 2016-10-03)
    Abstract: In most approaches for journal bearing rotor system diagnosis, dominant features are manually extracted based on expert’s experience and domain knowledge. With the adoption of advanced journal bearings and the limited knowledge about physics-of-failure, the current practice for feature extraction showed limitations for real applications in the power plant industry. To this end, this paper proposes an unsupervised scheme to extract features from correlated vibration signals. First, raw vibration signals from a pair of sensors are preprocessed by generating two-dimensional images. Second, the vibration images are characterized with a HOG (histogram of original gradients) descriptor. Then, deep learning is used to extract relevant features for journal bearing rotor system diagnosis. To demonstrate the validity of the proposed unsupervised-feature-extraction scheme, a case study was conducted with data from the RK4 rotor kit. The results showed that the proposed scheme outperformed existing methods in terms of fault classification accuracy. We anticipate that the proposed scheme is promising as it can minimize the reliance of expert’s experience and domain knowledge.
    Type of Medium: Online Resource
    ISSN: 2325-0178 , 2325-0178
    URL: Article
    DOI: 10.36001/phmconf.2016.v8i1.2533
    Language: Unknown
    Publisher: PHM Society
    Publication Date: 2016
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