IAMM: A maturity model for measuring industrial analytics capabilities in large-scale manufacturing facilities

Industrial big data analytics is an emerging multidisciplinary field, which incorporates aspects of engineering, statistics and computing, to produce data-driven insights that can enhance operational efficiencies, and produce knowledgebased competitive advantages. Developing industrial big data analytics capabilities is an ongoing process, whereby facilities continuously refine collaborations, workflows and processes to improve operational insights. Such activities should be guided by formal measurement methods, to strategically identify areas for improvement, demonstrate the impact of analytics initiatives, as well as deriving benchmarks across facilities and departments. This research presents a formal multi-dimensional maturity model for approximating industrial analytics capabilities, and demonstrates the model’s ability to assess the impact of an initiative undertaken in a real-world facility

Case study: the implementation of a data-driven industrial analytics methodology and platform for smart manufacturing

Integrated, real-time and open approaches relating to the development of industrial analytics capabilities are needed to support smart manufacturing. However, adopting industrial analytics can be challenging due to its multidisciplinary and cross-departmental (e.g. Operation and Information Technology) nature. These challenges stem from the significant effort needed to coordinate and manage teams and technologies in a connected enterprise. To address these challenges, this research presents a formal industrial analytics methodology that may be used to inform the development of industrial analytics capabilities. The methodology classifies operational teams that comprise the industrial analytics ecosystem, and presents a technology agnostic reference architecture to facilitate the industrial analytics lifecycle. Finally, the proposed methodology is demonstrated in a case study, where an industrial analytics platform is used to identify an operational issue in a largescale Air Handling Unit (AHU)

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 turbine's 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 fault

Enabling effective operational decision making on a Combined Heat and Power System using the 5C architecture

The use of Cyber Physical Systems (CPS) to optimise industrial energy systems is an approach which has the potential to positively impact on manufacturing sector energy efficiency. The need to obtain data to facilitate the implementation of a CPS in an industrial energy system is however a complex task which is often implemented in a non-standardised way. The use of the 5C CPS architecture has the potential to standardise this approach. This paper describes a case study where data from a Combined Heat and Power (CHP) system located in a large manufacturing company was fused with grid electricity and gas models as well as a maintenance cost model using the 5C architecture with a view to making effective decisions on its cost efficient operation. A control change implemented based on the cognitive analysis enabled via the 5C architecture implementation has resulted in energy cost savings of over €7400 over a four-month period, with energy cost savings of over €150,000 projected once the 5C architecture is extended into the production environment

Industry 4.0 driven statistical analysis of investment casting process demonstrates the value of digitalisation

The purpose of this research is to perform statistical data analysis of currently manually collected data in an area of the industrial manufacturing organisation employed in this study that is not digitalised to show the value that can be achieved through digitalisation. The insights gained through analysis of the data can be used to drive decision making in relation to the optimisation of input parameters to minimise the level of defective parts. The parts under investigation in this study were ceramic shells used in the manufacturing process of orthopaedic metal implants. The ceramic shell is a crucial element in the investment casting process because molten metal is poured into the ceramic shell to form the shape of the metal orthopaedic implant. Hence, by minimising the number of defective ceramic shells, there are fewer defective metal implants produced, resulting in cost savings and increased efficiency of the manufacturing process. A number of scientific questions to establish the relationship between the quantity of scrapped products and the level of the silica component in the ceramic slurry were defined and a series of independent t-tests were conducted to address these questions. The results from the t-tests showed the statistically optimal percentage of silica in the binder of the ceramic slurry to minimise the rate of a particular scrap type caused by thin or weak areas of the shell. These results demonstrate the value of analysing digital data relating to the manufacturing process to understand relationships between parameters in the manufacturing process and effectively root-cause scrap outputs. The results from the analysis gave rise to the implementation of a digitalised data collection system that allows continuous monitoring of the components in the ceramic slurry to ensure they are in the optimal specified range. Hence, the quality and yield rate of the orthopaedic implants are maintained at a high level. The digital data collection system also acts as a resource containing historical data for further potential scrap root-cause analysis

Total site heat integration in carbon neutral industrial manufacturing – A systematic mapping study

Carbon neutral operations are becoming important owing to tightening of greenhouse gas (GHG) emissions restrictions and increasing social corporate responsibility. Circular economy and total site integration are current hot topics, requiring resource-efficient industrial processes; this can be achieved with waste heat transfer and process integration. This study comprises a systematic mapping of research into waste heat transfer across total sites, with the goal of providing a structured overview of the research area to identify and quantify existing research. Fiive research questions are used to determine the publication landscape; the distribution of research categories, themes, modelling methodologies (Pinch, mathematical optimisation, hybrid); and the quantifiable benefits of waste heat transfer in terms of utility reduction, based on case studies. This work is expected to benefit researchers in understanding the research patterns and directions of total site heat integration, and in identifying the research gaps. Future work includes using the identified popular graphical modelling method of Pinch analysis for process integration of a medical device manufacturing site

The suitability of machine learning to minimise uncertainty in the measurement and verification of energy savings

Accurate energy modelling is a critical step in the measurement and verification (M&V) of energy savings, as a model for consumption in the baseline period is required. Machine learning (ML) algorithms offer an alternative approach to train these models with data-driven techniques. Industrial buildings offer the most challenging environment for the completion of M&V due to their complex energy systems. This paper investigates the novel use of ML algorithms for M&V of energy savings in industrial buildings. This approach enables the extension of the traditional project boundary also. The ML techniques applied consist of bi-variable and multi-variable ordinary least squares regression, decision trees, k-nearest neighbours, artificial neural networks and support vector machines. The prediction performances of the models are validated in the context of a biomedical manufacturing facility to find the optimal model parameters. Results show that models constructed using ML algorithms are more accurate than the conventional approach. A 51.09% reduction in error was achieved using the optimal model algorithm and parameters. The use of a higher measurement frequency reduced the spread of error across the six models. However, further analysis proved the use of more granular data did not always benefit model performance. Results of the sensitivity analysis showed the proposed ML approach to be beneficial in circumstances where missing baseline data limits the model training period length

Industrial smart and micro grid systems – A systematic mapping study

Energy efficiency and management is a fundamental aspect of industrial performance. Current research presents smart and micro grid systems as a next step for industrial facilities to operate and control their energy use. To gain a better understanding of these systems, a systematic mapping study was conducted to assess research trends, knowledge gaps and provide a comprehensive evaluation of the topic. Using carefully formulated research questions the primary advantages and barriers to implementation of these systems, where the majority of research is being conducted with analysis as to why and the relative maturity of this topic are all thoroughly evaluated and discussed. The literature shows that this topic is at an early stage but already the benefits are outweighing the barriers. Further incorporation of renewables and storage, securing a reliable energy supply and financial gains are presented as some of the major factors driving the implementation and success of this topic

From M&V to M&T: An artificial intelligence-based framework for real-time performance verification of demand-side energy savings

The European Union's Energy Efficiency Directive is placing an increased focus on the measurement and verification (M&V) of demand side energy savings. The objective of M&V is to quantify energy savings with minimum uncertainty. M&V is currently undergoing a transition to practices, known as M&V 2.0, that employ automated advanced analytics to verify performance. This offers the opportunity to effectively manage the transition from short-term M&V to long-term monitoring and targeting (M&T) in industrial facilities. The original contribution of this paper consists of a novel, robust and technology agnostic framework that not only satisfies the requirements of M&V 2.0, but also bridges the gap between M&V and M&T by ensuring persistence of savings. The approach features a unique machine learning-based energy modelling methodology, model deployment and an exception reporting system that ensures early identification of performance degradation. A case study demonstrates the effectiveness of the approach. Savings from a real-world project are found to be 177,962 +/- 12,334 kWh with a 90% confidence interval. The uncertainty associated with the savings is 8.6% of the allowable uncertainty, thus highlighting the viability of the framework as a reliable and effective tool