Evaluation and Comparison of Mixed Effects Model Based Prognosis for Hard Failure

Son, Junbo; Zhou, Qiang; Zhou, Shiyu; Mao, Xiaofeng; Salman, Mutasim

doi:10.1109/tr.2013.2259205

Cited by 50 publications

(27 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Usually the model-based approach makes use of mathematical models for the monitored system, but is difficult to model for a complex system (e.g. aircraft engines) [13], [14]. The experience-based prognostic approach uses probabilistic or stochastic models for the degradation by taking into account the data and the knowledge accumulated through experience, but the results are often not accurate enough to accommodate the engine's dynamic complex processes [15].…”

Section: Introductionmentioning

confidence: 99%

PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data

Wang

2014

IEEE Sensors J.

139

View full text Add to dashboard Cite

As the heart of an aircraft, the aircraft engine's condition directly affects the safety, reliability, and operation of the aircraft. Prognostics and health management for aircraft engines can provide advance warning of failure and estimate the remaining useful life. However, aircraft engine systems are complex with both intangible and uncertain factors, it is difficult to model the complex degradation process, and no single prognostic approach can effectively solve this critical and complicated problem. Thus, fusion prognostics is conducted to obtain more accurate prognostics results. In this paper, a prognostics and health management-oriented integrated fusion prognostic framework is developed to improve the system state forecasting accuracy. This framework strategically fuses the monitoring sensor data and integrates the strengths of the data-driven prognostics approach and the experience-based approach while reducing their respective limitations. As an application example, this developed fusion prognostics framework is employed to predict the remaining useful life of an aircraft gas turbine engine based on sensor data. The results demonstrate that the proposed fusion prognostics framework is an effective prognostics tool, which can provide a more accurate and robust remaining useful life estimation than any single prognostics method.Index Terms-Fusion prognostics, sensor data, prognostics and health management, remaining useful life, aircraft engines.

show abstract

Section: Introductionmentioning

confidence: 99%

PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data

Wang

2014

IEEE Sensors J.

139

View full text Add to dashboard Cite

show abstract

“…Various failure models have different triggers to initiate failure and to model failure propagation [189]. The uncertainty in physics-based prognosis approaches comes from assumptions and simplifications of model structures.…”

Section: Comparison and Evaluation Of Prognostic Modelsmentioning

confidence: 99%

Cloud-enabled prognosis for manufacturing

et al. 2015

View full text Add to dashboard Cite

Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes across spatial boundaries. These activities can improve accuracy and reliability in predicting resource needs and allocation, maintenance scheduling, and remaining service life of equipment. As an emerging infrastructure, cloud computing provides new opportunities to achieve the goals of advanced manufacturing. This paper reviews the historical development of prognosis theories and techniques and projects their future growth enabled by the emerging cloud infrastructure. Techniques for cloud computing are highlighted, as well as the influence of these techniques on the paradigm of cloud-enabled prognosis for manufacturing. Finally, this paper discusses the envisioned architecture and associated challenges of cloud-enabled prognosis for manufacturing

show abstract

“…The PV generator faults diagnosis [4][5][6][7] and prognosis [8][9][10][11][12] can stabilize its performance and ensure its availability and reliability. In this context, the paper objective is the development of an algorithm for the fault detection and diagnosis for a photovoltaic generator.…”

Section: Introductionmentioning

confidence: 99%

A smart algorithm for the diagnosis of short-circuit faults in a photovoltaic generator

Rezgui

Mouss

et al. 2014

2014 First International Conference on Green Energy ICGE 2014

View full text Add to dashboard Cite

This paper deals with a smart algorithm allowing short-circuit faults detection and diagnosis of PV generators. The proposed algorithm is based on the hybridization of a support vector machines (SVM) technique optimized by a k-NN tool for the classification of observations on the classifier itself or located in its margin.To test the proposed algorithm performance, a PV generator database containing observations distributed over classes is used for simulation purposes.

show abstract

Evaluation and Comparison of Mixed Effects Model Based Prognosis for Hard Failure

Cited by 50 publications

References 31 publications

PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data

PHM-Oriented Integrated Fusion Prognostics for Aircraft Engines Based on Sensor Data

Cloud-enabled prognosis for manufacturing

A smart algorithm for the diagnosis of short-circuit faults in a photovoltaic generator

Contact Info

Product

Resources

About