Dominic T.J. O’Sullivan

Big data in manufacturing: a systematic mapping study

The manufacturing industry is currently in the midst of a data-driven revolution, which promises to transform traditional manufacturing facilities in to highly optimised smart manufacturing facilities. These smart facilities are focused on creating manufacturing intelligence from real-time data to support accurate and timely decision-making that can have a positive impact across the entire organisation. To realise these efficiencies emerging technologies such as Internet of Things (IoT) and Cyber Physical Systems (CPS) will be embedded in physical processes to measure and monitor real-time data from across the factory, which will ultimately give rise to unprecedented levels of data production. Therefore, manufacturing facilities must be able to manage the demands of exponential increase in data production, as well as possessing the analytical techniques needed to extract meaning from these large datasets. More specifically, organisations must be able to work with big data technologies to meet the demands of smart manufacturing. However, as big data is a relatively new phenomenon and potential applications to manufacturing activities are wide-reaching and diverse, there has been an obvious lack of secondary research undertaken in the area. Without secondary research, it is difficult for researchers to identify gaps in the field, as well as aligning their work with other researchers to develop strong research themes. In this study, we use the formal research methodology of systematic mapping to provide a breadth-first review of big data technologies in manufacturing.

An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities

AbstractThe term smart manufacturing refers to a future-state of manufacturing, where the real-time transmission and analysis of data from across the factory creates manufacturing intelligence, which can be used to have a positive impact across all aspects of operations. In recent years, many initiatives and groups have been formed to advance smart manufacturing, with the most prominent being the Smart Manufacturing Leadership Coalition (SMLC), Industry 4.0, and the Industrial Internet Consortium. These initiatives comprise industry, academic and government partners, and contribute to the development of strategic policies, guidelines, and roadmaps relating to smart manufacturing adoption. In turn, many of these recommendations may be implemented using data-centric technologies, such as Big Data, Machine Learning, Simulation, Internet of Things and Cyber Physical Systems, to realise smart operations in the factory. Given the importance of machine uptime and availability in smart manufacturing, this research centres on the application of data-driven analytics to industrial equipment maintenance. The main contributions of this research are a set of data and system requirements for implementing equipment maintenance applications in industrial environments, and an information system model that provides a scalable and fault tolerant big data pipeline for integrating, processing and analysing industrial equipment data. These contributions are considered in the context of highly regulated large-scale manufacturing environments, where legacy (e.g. automation controllers) and emerging instrumentation (e.g. internet-aware smart sensors) must be supported to facilitate initial smart manufacturing efforts.

Automatically identifying and predicting unplanned wind turbine stoppages using SCADA and alarms system data: case study and results

Using 10-minute wind turbine SCADA data for fault prediction offers an attractive way of gaining additional prognostic capabilities without needing to invest in extra hardware. To use these data-driven methods effectively, the historical SCADA data must be labelled with the periods when the turbine was in faulty operation as well the sub-system the fault was attributed to. Manually identifying faults using maintenance logs can be effective, but is also highly time consuming and tedious due to the disparate nature of these logs across manufacturers, operators and even individual maintenance events. Turbine alarm systems can help to identify these periods, but the sheer volume of alarms and false positives generated makes analysing them on an individual basis ineffective. In this work, we present a new method for automatically identifying historical stoppages on the turbine using SCADA and alarms data. Each stoppage is associated with either a fault in one of the turbines sub-systems, a routine maintenance activity, a grid-related event or a number of other categories. This is then checked against maintenance logs for accuracy and the labelled data fed into a classifier for predicting when these stoppages will occur. Results show that the automated labelling process correctly identifies each type of stoppage, and can be effectively used for SCADA-based prediction of turbine faults.

Utilising the Cross Industry Standard Process for Data Mining to Reduce Uncertainty in the Measurement and Verification of Energy Savings

This paper investigates the application of Data Mining (DM) to predict baseline energy consumption for the improvement of energy savings estimation accuracy in Measurement and Verification (M&V). M&V is a requirement of a certified energy management system (EnMS). A critical stage of the M&V process is the normalisation of data post Energy Conservation Measure (ECM) to pre-ECM conditions. Traditional M&V approaches utilise simplistic modelling techniques, which dilute the power of the available data. DM enables the true power of the available energy data to be harnessed with complex modelling techniques. The methodology proposed incorporates DM into the M&V process to improve prediction accuracy. The application of multi-variate regression and artificial neural networks to predict compressed air energy consumption in a manufacturing facility is presented. Predictions made using DM were consistently more accurate than those found using traditional approaches when the training period was greater than two months.