Towards Design Principles for a Real-Time Anomaly Detection Algorithm Benchmark Suited to Industrie 4.0 Streaming Data

The vision of Industrie 4.0 includes the automated reduction of anomalies in flexibly combined production machine groups up to a zero-failure ideal. Algorithmic real-time detection of production anomalies may build the basis for machine self-diagnosis and self-repair during production. Several real-time anomaly detection algorithms appeared in recent years. However, different algorithms applied to the same data may result in contradictory detections. Thus, real-time anomaly detection in Industrie 4.0 machine groups may require a benchmark ranking for algorithms to increase detection results’ reliability. This paper makes a qualitative research contribution based on ten expert interviews to find design principles for such a benchmark ranking. The experts were interviewed on three categories, namely timeliness, thresholds and qualitative classification. The study’s results can be used as groundwork for a prototypical implementation of a benchmark

Practical and Adaptable Applications of Goal Programming: A Literature Review

Goal programming (GP) is an important optimization technique for handling multiple, and often conflicting, objectives in decision making. This paper undertakes an extensive literature review to synthesize key findings on the diverse real-world applications of GP across domains, its implementation challenges, and emerging directions. The introduction sets the context and objectives of the review. This is followed by an in-depth review of literature analyzing GP applications in areas as varied as agriculture, healthcare, education, energy management, supply chain planning, and macroeconomic policy modeling. The materials and methods provide an overview of the systematic literature review methodology. Key results are presented in terms of major application areas of GP. The discussion highlights the versatility and practical utility of GP, while also identifying limitations. The conclusion outlines promising avenues for enhancing GP modeling approaches to strengthen multi-criteria decision support

Predictive Maintenance in SCADA-Based Industries: A literature review

The purpose of this paper is to mapping and review what has been done on the topic of research on predictive maintenance in SCADA (Supervisory Control and Data Acquisition) based industries. In the research area of predictive maintenance, various methods for predicting damage or time to failure of a machine have been proposed and applied in various industries. This paper systematically categorizes predictive maintenance in SCADA-based industries research based on industry classifications according to ISIC (International Standard Industrial Classification of All Economic Activities). Furthermore, the research scope is explored its connection to the topics of Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Supervisory Control and Data Acquisition (SCADA). It is found that 81.5% of the research was conducted on the electricity, gas, steam, and air conditioning supply industries, 11.1% of research was conducted on the mining and quarrying industry, and 7.4% of the research conducted in the manufacturing industry. It is also found that 85.2% of studies used AI and ML, 18.5% of the studies used IoT, and 18.5% of research used AI/ML and IoT technology together