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

Chinedu Ossai

doi:10.3390/infrastructures2040020

Abstract

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.

Highlights

Management of asset integrity is one of the smartest things that organizations should do if they want to stay competitive in business
Kan et al [19] affirmed in the study of the state of prognosis of non-stationary and non-linear rotating systems that the effectiveness of failure and downtime prevention centers on data-driven statistical and artificial intelligence technologies. This implies that the use of different statistical and machine learning procedures such as Artificial Neural Network (ANN), Support Vector Machine (SVM), fuzzy logic, particle filters, the extended Kalman filter, Gaussian process regression, etc., is fundamental to the understanding of the deterioration trends of components of complex systems, since the proper utilization of the techniques could lead to actionable knowledge that will influence maintenance management decisions [20]
Processing of these data is vital for the prediction of the future state of the components, which is done by using different models such as ANN, Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Deep Learning (DL), Random Forest (RF), the Generalized Linear Model (GLM), Grid Search (GS) and Statistical Matching Performance Pattern (SMPP) [8,25,26,29,30,31,32,33]

Summary

Introduction

Management of asset integrity is one of the smartest things that organizations should do if they want to stay competitive in business. Kan et al [19] affirmed in the study of the state of prognosis of non-stationary and non-linear rotating systems that the effectiveness of failure and downtime prevention centers on data-driven statistical and artificial intelligence technologies This implies that the use of different statistical and machine learning procedures such as ANN, Support Vector Machine (SVM), fuzzy logic, particle filters, the extended Kalman filter, Gaussian process regression, etc., is fundamental to the understanding of the deterioration trends of components of complex systems, since the proper utilization of the techniques could lead to actionable knowledge that will influence maintenance management decisions [20]. A framework for integrating ANN-based big data analytics into real-time fault detection and identification for complex systems will be developed This will be achieved using future time prediction of the target component’s behavior, with the source/control components’ degradation information from historic SCADA sensor data. The successful implementation of the Infrastructures 2017, 2, 20 framework will make maintenance planning, inspection and repairs quicker, and at a reduced cost, due to the elimination of downtimes arising from unplanned maintenance schedules

Artificial Neural Network Concept

Frameworks for Complex System’s Prognosis

Fault Detection and Identification with ANN

Predicting Future Behavior of the Target Component

Findings

Conclusions