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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 non-functionality). 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 peer-reviewed 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 smart-troubleshooting 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.

In this paper, we tackle the challenge of generatingsynthetic log files using generative adversarial networks to sup-port smart-troubleshooting experimentation. Log files are criticalfor implementing monitoring systems for smart-troubleshooting,as they capture valuable information about the activities andevents occurring within the monitored system. Analyzing theselogs is crucial for effective smart-troubleshooting, enhancing theoverall efficiency, reliability, and security of smart manufacturingprocesses. However, accessing public log data is difficult due toprivacy concerns and the need to protect sensitive information.Moreover, for the purpose of effective troubleshooting, it isessential to have datasets that include fault, error, and failure logsas well as standard logs. In recent years, synthetic log files haveemerged as a promising solution to augment limited real-worlddatasets and facilitate the development and evaluation of anomalydetection techniques. Building on this concept of synthetic data,we have developed a specific log generation technique and datasettailored for testing smart-troubleshooting techniques in heteroge-neous connected systems environments, such as industrial cyber-physical systems and the internet of things. First, we propose amethodology that generates synthetic log files based on generativeadversarial networks. Later, we instantiate this methodologyusing different Generative Adversarial Network implementationsand present a validation and a comprehensive comparativeanalysis of their performance. Eventually, we provide a robustdataset for anomaly detection and threat analysis in cyberspacesecurity. Based on the results of our comparison, CTGAN hasshown superior performance in generating high-quality syntheticlog files.