ORCID Profile
0000-0002-2526-1376
Current Organisation
China University of Mining and Technology
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Publisher: American Scientific Publishers
Date: 04-2017
Abstract: Numerical modeling has been recognized as the dispensable tools for mechanical fault mechanism analysis. Techniques, ranging from macro to nano levels, include the finite element modeling boundary element modeling, modular dynamic modeling, nano dynamic modeling and so forth. This work firstly reviewed the progress on the fault mechanism analysis for gear transmissions from the tribological and dynamic aspects. Literature review indicates that the tribological and dynamic properties were separately investigated to explore the fault mechanism in gear transmissions. However, very limited work has been done to address the links between the tribological and dynamic properties and scarce researches have been done for coal cutting machines. For this reason, the tribo-dynamic coupled model was introduced to bridge the gap between the tribological and dynamic models in fault mechanism analysis for gear transmissions in coal cutting machines. The modular dynamic modeling and nano dynamic modeling techniques are expected to establish the links between the tribological and dynamic models. Possible future research directions using the tribo dynamic coupled model were summarized to provide potential references for researchers in the field.
Publisher: Elsevier BV
Date: 08-2016
Publisher: IOP Publishing
Date: 12-05-2016
Publisher: Elsevier BV
Date: 08-2016
Publisher: SAGE Publications
Date: 26-12-2017
Abstract: This work attempts to introduce a new intelligent method for condition monitoring of diesel engines. Diesel engine is one of the most important power providers for various industrial applications, including automobiles, ships, agricultures, construction, and electrical machinery. Due to harsh working environment, diesel engines are vulnerable to failures. This article addresses a significant need to improve predictive maintenance activities in diesel engines. A new failure diagnostics approach was proposed based on the manifold learning and swarm intelligence optimized multiclass multi-kernel relevant vector machine. Three manifold learning algorithms were first respectively used to fuse the features that extracted from the original vibration data of the diesel engines into a new nonlinear space. The fused features contain the most distinct health information of the engine by discarding redundant features. Then, the swarm intelligence optimized multiclass multi-kernel relevant vector machine was proposed to identify the failures using the fused features. The contribution of this research is that the dragonfly algorithm is employed to optimize the weights of the multi-kernel functions in the multiclass relevant vector machine. It was also applied to establishing a weighted-sum model by combining the outputs of swarm intelligence optimized multiclass multi-kernel relevant vector machine models with different manifold learning algorithms. Robust failure detection of diesel engines is achieved owing to combined strengths of multiple kernel functions and weighted-sum strategy. The effectiveness of the proposed method is demonstrated by experimental vibration data collected from a commercial diesel engine. The failure detection capability of the proposed manifold learning and swarm intelligence optimized multiclass multi-kernel relevant vector machine method for diesel engines will potentially benefit the machine condition monitoring industry by improving budgeting/forecasting and/or enabling just-in-time maintenance.
Publisher: IEEE
Date: 03-2010
Publisher: Springer Science and Business Media LLC
Date: 28-10-2015
Publisher: Elsevier BV
Date: 02-2018
Publisher: Springer Science and Business Media LLC
Date: 20-11-2015
No related grants have been discovered for Yu Jiang.