Abstract: The disclosure relates to anomaly detection in an industrial environment including multiple industrial units and systems, generating huge volume of data. The conventional methods rely only on sensor data alone. The techniques of handling missing data plays a crucial role in determining the performance of industrial anomaly detection system. Further, imputation of missing data could cause error in computation, thus affecting the accuracy of the industrial anomaly detection system. The present disclosure addresses the problems associated with missing data by utilizing a masking technique. Further, the present disclosure utilizes quantitative and qualitative metadata associated with industrial system along with the sensor data to improve anomaly detection performance. Furthermore, the present disclosure includes a model recommendation system which provides transfer learning based utilization of existing models for similar industrial systems.

Abstract: Data is prone to various attacks such as cyber-security attacks, in any industry. State of the art systems in the domain of data security fail to identify adversarial attacks in real-time, and this leads to security issues, as well as results in the process/system providing unintended results. The disclosure herein generally relates to data security analysis, and, more particularly, to a method and system for assessing impact of adversarial attacks on time series data and providing defenses against such attacks. The system performs adversarial attacks on a selected data-driven model to determine impact of the adversarial attacks on the selected data model, and if the impact is such that performance of the selected data model is less than a threshold, then the selected data model is retrained.

Abstract: Industrial plants involve a large amount of equipment, which generate a large amount of data. By analyzing this data, the operator can diagnose anomaly in the plant. Analyzing this data is difficult and time taking task. A method and system for diagnosing anomaly in an industrial system in a time efficient and convenient manner has been provided. The system is configured to diagnose the anomaly by finding out one or more sensors responsible for the anomaly. The present disclosure treats the anomaly detection model as a score generating function. Whenever for a particular instance the score given by the anomaly detection model crosses a pre-determined threshold, anomaly is reported and the diagnosis algorithm is triggered. The system is configured to diagnose the anomaly predicted in case of time series as well as non-time series data.

Abstract: A system and method for performing data-based optimization of performance indicators of process and manufacturing plants. The system consists of modules for collecting and merging data from industrial processing units, pre-processing the data to remove outliers and missingness. Further, the system generates customized outputs from data and identifies important variables that affect a given process performance indicator. The system also builds predictive models for key performance indicators comprising the important features and determines operating points for optimizing the key performance indicators with minimum user intervention. In particular, the system receives inputs from users on the key performance indicators to be optimized and notifies the users of outputs from various steps in the analysis that help the users to effectively manage the analysis and take appropriate operational decisions.

Abstract: A system and method for performing data-based optimization of performance indicators of process and manufacturing plants. The system consists of modules for collecting and merging data from industrial processing units, pre-processing the data to remove outliers and missingness. Further, the system generates customized outputs from data and identifies important variables that affect a given process performance indicator. The system also builds predictive models for key performance indicators comprising the important features and determines operating points for optimizing the key performance indicators with minimum user intervention. In particular, the system receives inputs from users on the key performance indicators to be optimized and notifies the users of outputs from various steps in the analysis that help the users to effectively manage the analysis and take appropriate operational decisions.