Remaining useful life prediction of grinding mill liners using an artificial neural network

Ahmadzadeh, Farzaneh; Lundberg, Jan

doi:10.1016/j.mineng.2013.05.026

Cited by 62 publications

(33 citation statements)

References 18 publications

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“…The output of ANN is RUL prediction or performance degradation assessment, which is used for conducting effective maintenance strategies. ANNs widely used in fault prediction include BPNN [91][92][93][94][95], radial basis function network (RBFN), and RNN [96]. Ahmadzadeh, et al [94], proposed a three-layer feedforward BPNN for RUL estimation of grinding mill liners, which considered degeneration and condition monitoring data as the inputs of ANN, and used RUL as the output of ANN.…”

Section: Annmentioning

confidence: 99%

“…ANNs widely used in fault prediction include BPNN [91][92][93][94][95], radial basis function network (RBFN), and RNN [96]. Ahmadzadeh, et al [94], proposed a three-layer feedforward BPNN for RUL estimation of grinding mill liners, which considered degeneration and condition monitoring data as the inputs of ANN, and used RUL as the output of ANN. Rodriguez, et al [95], presented ANN (six input layers, three hidden layers, and one output layer) to predict and simulate the behavior of life-cycle assessment in blades of steam turbines.…”

Section: Annmentioning

confidence: 99%

See 1 more Smart Citation

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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Section: Annmentioning

confidence: 99%

Section: Annmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

show abstract

“…105 At completion of a training process, the MLP is capable of giving output solution for any new input based on the generalized mapping that has been developed. 106 2. RNN.…”

Section: Ann-based Modelsmentioning

confidence: 99%

Multidimensional prognostics for rotating machinery: A review

Duan

Mba

et al. 2017

Advances in Mechanical Engineering

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Determining prognosis for rotating machinery could potentially reduce maintenance costs and improve safety and availability. Complex rotating machines are usually equipped with multiple sensors, which enable the development of multidimensional prognostic models. By considering the possible synergy among different sensor signals, multivariate models may provide more accurate prognosis than those using single-source information. Consequently, numerous research papers focusing on the theoretical considerations and practical implementations of multivariate prognostic models have been published in the last decade. However, only a limited number of review papers have been written on the subject. This article focuses on multidimensional prognostic models that have been applied to predict the failures of rotating machinery with multiple sensors. The theory and basic functioning of these techniques, their relative merits and drawbacks and how these models have been used to predict the remnant life of a machine are discussed in detail. Furthermore, this article summarizes the rotating machines to which these models have been applied and discusses future research challenges. The authors also provide seven evaluation criteria that can be used to compare the reviewed techniques. By reviewing the models reported in the literature, this article provides a guide for researchers considering prognosis options for multi-sensor rotating equipment.

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“…Previous studies have tried to predict the RUL based on time series [18,19]. However, nowadays it is more useful to use models based on the monitoring of the operation variables values during the operation of the equipment [20,21]. Therefore, the present study uses this approach.…”

Section: Introductionmentioning

confidence: 99%

A New Predictive Model Based on the ABC Optimized Multivariate Adaptive Regression Splines Approach for Predicting the Remaining Useful Life in Aircraft Engines

et al. 2016

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Abstract:Remaining useful life (RUL) estimation is considered as one of the most central points in the prognostics and health management (PHM). The present paper describes a nonlinear hybrid ABC-MARS-based model for the prediction of the remaining useful life of aircraft engines. Indeed, it is well-known that an accurate RUL estimation allows failure prevention in a more controllable way so that the effective maintenance can be carried out in appropriate time to correct impending faults. The proposed hybrid model combines multivariate adaptive regression splines (MARS), which have been successfully adopted for regression problems, with the artificial bee colony (ABC) technique. This optimization technique involves parameter setting in the MARS training procedure, which significantly influences the regression accuracy. However, its use in reliability applications has not yet been widely explored. Bearing this in mind, remaining useful life values have been predicted here by using the hybrid ABC-MARS-based model from the remaining measured parameters (input variables) for aircraft engines with success. A correlation coefficient equal to 0.92 was obtained when this hybrid ABC-MARS-based model was applied to experimental data. The agreement of this model with experimental data confirmed its good performance. The main advantage of this predictive model is that it does not require information about the previous operation states of the aircraft engine.

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Remaining useful life prediction of grinding mill liners using an artificial neural network

Cited by 62 publications

References 18 publications

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Multidimensional prognostics for rotating machinery: A review

A New Predictive Model Based on the ABC Optimized Multivariate Adaptive Regression Splines Approach for Predicting the Remaining Useful Life in Aircraft Engines

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