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The impact of an anomaly is domain-dependent. In a dataset of network activities, an anomaly can imply an intrusion attack. Other objectives of anomaly detection are industrial damage detection, data leak prevention, identifying security vulnerabilities or military surveillance. Anomalies are observations or a sequence of observations in which the distribution deviates remarkably from the general distribution of the whole dataset. A large majority of the dataset consists of normal (healthy) data points. The anomalies form only a very small part of the dataset. Anomaly detection is the technique used to find these observations and its methods are specific to the type of data. While there is a wide spectrum of anomaly detection approaches available today, it becomes increasingly difficult to keep track of all the techniques. In fact, it is not clear which of the three categories of detection methods, ie statistical approaches, machine learning approaches or deep learning approaches, is more appropriate to detect anomalies in time-series data, which are mainly used in industry. A typical industrial device has multi-dimensional characteristics. It is possible to measure voltage, current, active power, vibrations, rotational speed, temperature, pressure difference, etc, on such a device. Early detection of the anomalous behaviour of industrial devices can help reduce or prevent serious damage, which could lead to significant financial loss. This paper presents a summary of the methods used to detect anomalies in condition monitoring applications. |
