Reliability Assessment of Repairable Systems Using Simple Regression Models

Álvarez, Miguel Ángel Navas; Ibáñez, José Carpio; Sancho, Carlos

doi:10.33889/ijmems.2021.6.1.011

Cited by 6 publications

(1 citation statement)

References 31 publications

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“…The traditional methods of reliability evaluation are not applicable in the case of small samples. In response to the shortcomings of the traditional reliability evaluation methods, a data-driven reliability evaluation method has been proposed (Álvarez et al, 2021;Sharma and Rai, 2021). However, how to make an accurate evaluation of component reliability based on limited fault data is still one of the challenges in existing research.…”

Section: Introductionmentioning

confidence: 99%

Reliability Evaluation and Prediction Method with Small Samples

Dui

Dong

Tao

2023

Int. j. math. eng. manag. sci.

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How to accurately evaluate and predict the degradation state of the components with small samples is a critical and practical problem. To address the problems of unknown degradation state of components, difficulty in obtaining relevant environmental data and small sample size in the field of reliability prediction, a reliability evaluation and prediction method based on Cox model and 1D CNN-BiLSTM model is proposed in this paper. Taking the historical fault data of six components of a typical load-haul-dump (LHD) machine as an example, a reliability evaluation method based on Cox model with small sample size is applied by comparing the reliability evaluation models such as logistic regression (LR) model, support vector machine (SVM) model and back propagation neural network (BPNN) model in a comprehensive manner. On this basis, a reliability prediction method based on one-dimensional convolutional neural network-bi-directional long and short-term memory network (1D CNN-BiLSTM) is applied with the objective of minimizing the prediction error. The applicability as well as the effectiveness of the proposed model is verified by comparing typical time series prediction models such as the autoregressive integrated moving average (ARIMA) model and multiple linear regression (MLR). The experimental results show that the proposed model is valuable for the development of reliability plans and for the implementation of reliability maintenance activities.

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Section: Introductionmentioning

confidence: 99%

Reliability Evaluation and Prediction Method with Small Samples

Dui

Dong

Tao

2023

Int. j. math. eng. manag. sci.

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Reliability analysis of m -out-of- r -within- k -out-of- n system

Tyagi

Kumar

Ram

2022

Communications in Statistics - Simulation and Computation

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Predictive Maintenance Planning Using a Hybrid ARIMA-ANN Model

Kaynak,

Ervural

2024

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Predicting machine faults is crucial for maintaining operational efficiency in industrial settings, minimizing unplanned downtime, and ensuring customer satisfaction. Fault prediction helps identify faults and create maintenance schedules. Maintenance planning involves strategically scheduling activities to ensure the continuous operational efficiency of systems. This study focuses on reducing unplanned downtime in a food company by developing a predictive maintenance plan through machine fault prediction. Artificial Neural Networks (ANNs) are excellent in handling non-linear models, while the ARIMA model is adequate for linear models. However, real-world data often contains linear and non-linear elements, requiring hybrid models for improved accuracy. This study employs ARIMA, ANNs, and a Hybrid ARIMA-ANN model. The dataset is individually modelled using each approach. Using a 3-month machine fault dataset, predictive values for machine fault times are generated and statistically evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The findings indicate that the hybrid model outperforms both ARIMA and ANN models. The food company can significantly reduce unplanned downtime and ensure operational efficiency using a hybrid model. Predictive maintenance planning can help the food company save costs and maintain a competitive edge in the market.

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Reliability Assessment of Repairable Systems Using Simple Regression Models

Cited by 6 publications

References 31 publications

Reliability Evaluation and Prediction Method with Small Samples

Reliability Evaluation and Prediction Method with Small Samples

Reliability analysis of m -out-of- r -within- k -out-of- n system

Predictive Maintenance Planning Using a Hybrid ARIMA-ANN Model

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