Analysis of log files to enable smart-troubleshooting in Industry 4.0: a systematic mapping study

Abstract

A crucial element of Industry 4.0, is the utilization of smart devices that generate log files. Log files are key components containing data on system operations, faults (unexpected glitches or malfunctions), errors (mistakes or incorrect actions), and failures (complete breakdowns or nonfunctionality). This paper presents a systematic mapping study analyzing research conducted on log files for smart-troubleshooting in Industry 4.0. To the best of our knowledge, this is the study that aims to identify research trends, log file attributes, techniques, and challenges involved in log file analysis for smart-troubleshooting. From an initial set of 941 potentially relevant peerreviewed publications, 74 primary studies were selected and analyzed using a meticulous data extraction, analysis, and synthesis process. The results of the study demonstrate that the majority of research has focused on developing algorithms for log analysis, with machine learning being the most commonly used approach. The smart-troubleshooting encompasses a range of activities and tools that are essential for collecting failure data generated by diverse interconnected devices, conducting analyses, and aligning them with troubleshooting instructions and software remedies. Moreover, the study identifies the need for further research in the areas of real-time log analysis, anomaly detection, and the integration of log analysis with other Industry 4.0 technologies. In conclusion, our study provides insights into the current state of research in log analysis for smarttroubleshooting in Industry 4.0 and identifies areas for future research. The use of smart devices generating log files in Industry 4.0 highlights the importance of log file analysis for troubleshooting purposes. Further research is needed to address the challenges and opportunities in this field to integrate log analysis with other Industry 4.0 technologies for performing more efficient and effective troubleshooting.

Machine Learning-Supported Designing of Human–Machine Interfaces

“…The concept of the proposed method can be seen in Figure 1. An essential condition for the development of HMI supported by ML is a properly designed log file [20]. For example, a process event in the log file can be an alarm, an adjustment of a process variable, a measured process value signal, or an alarm acknowledgement.…”