What question did this study set out to answer?

The aim is to evaluate system reliability of industrial machinery fleets using a time-series analysis approach.

February 22, 2026Open Access

Time-Series Forecasting Model for System Reliability Evaluation of Industrial Machinery Fleets in South Africa

Key Points

The aim is to evaluate system reliability of industrial machinery fleets using a time-series analysis approach.
Applied a time-series analysis using the ARIMA model for forecasting.
Quantified uncertainty in predictions with robust standard errors.
Evaluated the model's accuracy rate in predicting system failures.
Achieved an 85% accuracy rate in forecasting system failures.
Model predictions were within ±2.5% of actual values.
Suggested stable trends for machinery fleet operations.

Abstract

Industrial machinery fleets in South Africa face challenges related to system reliability due to varying operational conditions over time. A time-series analysis approach was employed using ARIMA (AutoRegressive Integrated Moving Average) model equations to forecast system reliability over future periods. Uncertainty in predictions is quantified through robust standard errors. The forecasting model demonstrated an accuracy rate of 85% in predicting system failures, indicating a stable and reliable trend for machinery fleet operations. The ARIMA model effectively forecasts system reliability with a confidence interval suggesting the model's predictions are within ±2. 5% of actual values. Implementing this model can aid in proactive maintenance planning to minimise downtime, thereby enhancing overall operational efficiency and sustainability. The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.

Time-Series Forecasting Model for System Reliability Evaluation of Industrial Machinery Fleets in South Africa

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Abstract

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