Purpose -To propose an accurate product reliability prediction model in order to enhance product quality and reduce product costs. Design/methodology/approach -This study proposes a method for analysing and forecasting field failure data for repairable systems. The novel method constructs a predictive model by combining the seasonal autoregressive integrated-moving average (SARIMA) method and neural network model. Findings -Current methods for analysing and forecasting field failure data for repairable systems do not consider the seasonal effect in the data. The proposed method can not only analyse the trends and seasonal vibration of the data, but can also forecast the short-and long-term reliability of the system based on only a small amount of historical data. Research limitations/implications -This study adopts only real failure data from an electronic system to verify the feasibility and effectiveness of the proposed method. Future research may use other product's failure data to verify the proposed method. Practical implications -Results in this study can provide a valuable reference for engineers when constructing quality feedback systems for assessing current quality conditions, providing logistical support, correcting product design, facilitating optimal component-replacement and maintenance strategies, and ensuring that products meet quality requirements. Originality/value -The proposed method is superior to other prediction techniques in predicting future real failure data.

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With the development of renewable energy, renewable energy incubators have emerged continuously. However, these incubators present a crude development model of low-level replication and large-scale expansion, which has triggered a series of urgent problems including unbalanced regional development, low incubation efficiency, low resource utilization, and vicious competition for resources. There are huge challenges for the sustainable development of incubators in the future. A scientific and accurate evaluation approach is of great significance for improving the sustainability of renewable energy incubators. Therefore, this paper proposes a novel method combining an interval type-II fuzzy analytic hierarchy process (AHP) with mind evolutionary algorithm-modified least-squares support vector machine (MEA-MLSSVM). The indicator system is established from two aspects: service capability and operational efficiency. TOPSIS integrated with an interval type-II fuzzy AHP is employed for index weighting and assessment. In the least-squares support vector machine (LSSVM), the traditional radial basis function is replaced with the wavelet transform function (WT), and the parameters are fine-tuned by the mind evolutionary algorithm (MEA). Accordingly, the establishment of a comprehensive sustainability evaluation model for renewable energy incubators is accomplished in this paper. The experimental study reveals that this novel technique has the advantages of scientificity and precision and provides a decision-making basis for renewable energy incubators to realize sustainable operation.