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Real-time prediction of the state of complex systems is vital for integrity management since it is easier to plan for asset maintenance, reduce risks associated with unplanned downtime and reduce the cost of maintenance. This study utilized a four-fold cross-validation ensemble for an Artificial Neural Network (ANN) that used Multi-Layer Perceptron (MLP) in a backward propagation technique for haul crane prognosis. Big data on components’ degradation states obtained from the Supervisory Control And Data Acquisition (SCADA) systems were used to implement the study. After preprocessing the dataset, importance scoring was used to compute the Cumulative Target-component Percentage-influence (CTP) of the input variables (source components) on the output variable (the target component) at the 95.5%, 99.3%, 99.9% and 100% levels. The specific source components responsible for the CTP levels of the target component were later used for the ANN network training that followed the cross-validation ensemble technique. The cross-validation ensemble ANN technique was also compared to the classic ANN and other machining learning algorithms. Finally, the best-trained cross-validation ensemble ANN network, which was obtained at the 99.9% CTP level, was used for future estimation of the time of failure of the system to enhance planning for the expected maintenance program that will be required at such times

Integrated Big Data Analytics Technique for Real-Time Prognostics, Fault Detection and Identification for Complex Systems