DeepAD

• Condition monitoring
• Predictive maintenance
• Abnormal behavior detection

Input(s):

• Sensors’ data or data from other sources in timeseries format

Main feature(s):

• Deep Learning-based Anomaly Detection tool
• Suitable for unsupervised datasets
• Autoencoders (AE) and LongShort Term Memory (LSTM) Neural Networks

Output(s):

• Discovers patterns in data that do not conform to the expected normal behaviour

Input data in a sequence format with time sample.

Software developers and designers of industrial application need to implement Anomaly Detection techniques. It has applications in cyber-security intrusion detection, fraud detection, fault detection, system health monitoring, event detection in sensor networks, detecting ecosystem disturbances, defect detection, and medical diagnosis.

Licensing and permission required.