  Rotary machine breakdown detection systems are outdated and dependent upon
routine testing to discover faults. This is costly and often reactive in
nature. Real-time monitoring offers a solution for detecting faults without the
need for manual observation. However, manual interpretation for threshold
anomaly detection is often subjective and varies between industrial experts.
This approach is ridged and prone to a large number of false positives. To
address this issue, we propose a Machine Learning (ML) approach to model normal
working operation and detect anomalies. The approach extracts key features from
signals representing known normal operation to model machine behaviour and
automatically identify anomalies. The ML learns generalisations and generates
thresholds based on fault severity. This provides engineers with a traffic
light system were green is normal behaviour, amber is worrying and red
signifies a machine fault. This scale allows engineers to undertake early
intervention measures at the appropriate time. The approach is evaluated on
windowed real machine sensor data to observe normal and abnormal behaviour. The
results demonstrate that it is possible to detect anomalies within the amber
range and raise alarms before machine failure.
