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Linear And Nonlinear Programming

The Engineering Design of Systems – Models and Methods

Firms do not currently fully appreciate the complex characteristics of Industry 4.0 and as a result are uncertain about what it represents for them. In this study, an assessment model is developed ...

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Defining and assessing industry 4.0 maturity levels–case of the defence sector

Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business. Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware. Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security. Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to real-time big data analytics and cybersecurity.

/pdf/manufacturing-big-data-ecosystem-a-systematic-literature-qykba7oqcb.pdf

Manufacturing big data ecosystem: A systematic literature review

Smart manufacturing is strongly correlated with the digitization of all manufacturing activities. This increases the amount of data available to drive productivity and profit through data-driven decision making programs. The goal of this article is to assist data engineers in designing big data analysis pipelines for manufacturing process data. Thus, this paper characterizes the requirements for process data analysis pipelines and surveys existing platforms from academic literature. The results demonstrate a stronger focus on the storage and analysis phases of pipelines than on the ingestion, communication, and visualization stages. Results also show a tendency towards custom tools for ingestion and visualization, and relational data tools for storage and analysis. Tools for handling heterogeneous data are generally well-represented throughout the pipeline. Finally, batch processing tools are more widely adopted than real-time stream processing frameworks, and most pipelines opt for a common script-based data processing approach. Based on these results, recommendations are offered for each phase of the pipeline.

https://link.springer.com/content/pdf/10.1186%2Fs40537-018-0162-3.pdf

Manufacturing process data analysis pipelines: a requirements analysis and survey

The high complexity of manufacturing processes and the continuously growing amount of data lead to excessive demands on the users with respect to process monitoring, data analysis and fault detection. For these reasons, problems and faults are often detected too late, maintenance intervals are chosen too short and optimization potential for higher output and increased energy efficiency is not sufficiently used. A possibility to cope with these challenges is the development of self-learning assistance systems, which identify relevant relationships by observation of complex manufacturing processes so that failures, anomalies and need for optimization are automatically detected. The assistance system developed in the present work accomplishes data acquisition, process monitoring and anomaly detection in industrial and agricultural processes. The assistance system is evaluated in three application cases: Large distillation columns, agricultural harvesting processes and large-scale sorting plants. In this paper, the developed infrastructures for data acquisition in these application cases are described as well as the developed algorithms and initial evaluation results.

https://iopscience.iop.org/article/10.1088/1742-6596/659/1/012055/pdf

Big Data Analysis of Manufacturing Processes

In the presented work, the detection of anomalous energy consumption in hybrid industrial production systems is investigated A model-based approach with a timed hybrid automaton as overall system model is employed for anomaly detection The approach is based on the assumption of several system modes, ie phases with continuous system behavior Transitions between the modes are attributed to discrete control events such as on/off signals The underlying discrete event system which comprises both system modes and transitions is modeled as finite state machine The focus of this paper is set on the modeling of the energy consumption in the particular system modes Sequences of stochastic state space models are employed for this purpose Model learning and anomaly detection for this approach are considered The proposed approach is further evaluated in a small model factory The experimental results show significant improvements compared to existing approaches to anomaly detection in hybrid industrial systems

A stochastic method for the detection of anomalous energy consumption in hybrid industrial systems

Manufacturing industry is facing major challenges to meet customer requirements, which are constantly changing. Therefore, products have to be manufactured with efficient processes, minimal interruptions, and low resource consumptions. To achieve this goal, huge amounts of data generated by industrial equipment needs to be managed and analyzed by modern technologies. Since the big data era in manufacturing industry is still at an early stage, there is a need for a reference architecture that incorporates big data and machine learning technologies and aligns with the Industrie 4.0 standards and requirements. In this paper, requirements for designing a scalable analytics platform for industrial data are derived from Industrie 4.0 standards and literature. Based on these requirements, a reference big data architecture for industrial machine learning applications is proposed and compared to related works. Finally, the proposed architecture has been implemented in the Lab Big Data at the SmartFactoryOWL and its scalability and performance have been evaluated on parallel computation of an industrial PCA model. The results show that the proposed architecture is linearly scalable and adaptable to machine learning use cases and will help to improve the industrial automation processes in production systems.

Scalable Analytics Platform for Machine Learning in Smart Production Systems

This paper addresses the optimization of energy flows between electric drives in conveying systems. Thereby, load peaks and the feedback of electric energy into the grid are reduced. The approach is based on the solution of a mixed integer quadratic optimization problem which incorporates models for energy flows and energy consumption of the electric drives. Models for energy flows and energy consumption can be parametrized from sensor data. Besides, movement constraints such as start positions, end positions and time limits are taken into account. The solution of the optimization problem is accomplished in realtime by application of standard solvers. Experimental results show that the proposed methods allows recovering regenerative energy of electric drives as motoric power for other drives. Integration into material flow planning of an automated warehouse is straightforward so that an inexpensive and simply usuable way for power saving in intralogistics is presented.

Energy efficiency optimization by automatic coordination of motor speeds in conveying systems

In this paper, stochastic models for fault detection in industrial automation processes are investigated. Thereby, nonlinear, time-variant systems are considered. The basic idea consists in building a probability distribution model and evaluating the likelihood of observations under that model. In contrast to the existing methods, this paper considers the practically important case in which measurement noise is negligible and all process variables are observable. This assumption allows the direct evaluation of a probability distribution for fault detection without approximations such as second order statistics or particles. The main part of this paper deals with adequate models for this probability distribution such as Gaussian and Hidden Markov models. Such models require predictions of the expectation values of the respective probability distributions. Regression models such as (multivariate) linear regression models and neural networks are investigated for this purpose. Evaluations are conducted with respect to prediction accuracies and fault detection capabilities of the employed models. Evaluations show superior results of the novel approach compared to existing fault detection methods, which are based on approximations such as second order statistics.

Stefan Windmann

Papers

Big Data Analysis of Manufacturing Processes

A stochastic method for the detection of anomalous energy consumption in hybrid industrial systems

Scalable Analytics Platform for Machine Learning in Smart Production Systems

Energy efficiency optimization by automatic coordination of motor speeds in conveying systems

Efficient fault detection for industrial automation processes with observable process variables