  Anomaly detection is critical in the smart industry for preventing equipment
failure, reducing downtime, and improving safety. Internet of Things (IoT) has
enabled the collection of large volumes of data from industrial machinery,
providing a rich source of information for Anomaly Detection. However, the
volume and complexity of data generated by the Internet of Things ecosystems
make it difficult for humans to detect anomalies manually. Machine learning
(ML) algorithms can automate anomaly detection in industrial machinery by
analyzing generated data. Besides, each technique has specific strengths and
weaknesses based on the data nature and its corresponding systems. However, the
current systematic mapping studies on Anomaly Detection primarily focus on
addressing network and cybersecurity-related problems, with limited attention
given to the industrial sector. Additionally, these studies do not cover the
challenges involved in using ML for Anomaly Detection in industrial machinery
within the context of the IoT ecosystems. This paper presents a systematic
mapping study on Anomaly Detection for industrial machinery using IoT devices
and ML algorithms to address this gap. The study comprehensively evaluates 84
relevant studies spanning from 2016 to 2023, providing an extensive review of
Anomaly Detection research. Our findings identify the most commonly used
algorithms, preprocessing techniques, and sensor types. Additionally, this
review identifies application areas and points to future challenges and
research opportunities.
