Anomaly vs Fault Detection
Anomaly vs Fault Detection
Fault detection is not anomaly detection. It’s based on a model of system. Anomaly detection just tells you what’s unusual, not what’s bad.
Anomaly detection and fault detection are both techniques used to identify unusual or unexpected behavior in a system, but they differ in their goals and methodologies.
Anomaly detection is a broader concept that involves identifying any deviation from normal behavior in a system. Anomalies can be either benign or harmful and may not necessarily indicate a fault. Anomaly detection techniques typically involve statistical analysis, machine learning algorithms, or rule-based methods to identify abnormal patterns.
On the other hand, fault detection is a more specific concept that focuses on identifying faults or failures in a system that may cause it to malfunction. Faults are typically harmful and can impact the performance or safety of a system. Fault detection techniques often involve monitoring system parameters or signals and comparing them to pre-defined thresholds to detect abnormal behavior that may indicate a fault.
In summary, while both anomaly detection and fault detection are used to identify unusual behavior, anomaly detection is broader and can detect both benign and harmful anomalies, while fault detection is more specific and focuses on identifying faults that can harm the system.
Anomaly: two class classification.
Anomaly detection is a classification problem, but it is not a two-class classification problem in the traditional sense of binary classification, where the goal is to classify data instances into one of two classes (e.g., positive or negative, 1 or 0).
Instead, anomaly detection is typically a one-class classification problem, where the goal is to build a model that can identify instances that are significantly different from the majority of the data. In this case, the majority class is considered to be the normal or expected behavior, and the minority class is the anomalous behavior. The model is trained on the normal data and is then used to classify new data as either normal or anomalous.
Some anomaly detection methods may use a two-class classification approach, where the goal is to classify data into either normal or anomalous classes. However, this is usually done in cases where labeled data is available for both normal and anomalous behavior, and the focus is on building a classifier that can accurately classify new data as either normal or anomalous.