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: This disclosure relates to method and system for training of deep learning based time-series models based on self-supervised learning. The problem of missing data is taken care of by introducing missing-ness masks. The deep learning model for univariate and multivariate time series data is trained with the distorted input data using the self-supervised learning to reconstruct the masked input data. Herein, the one or more distortion techniques include quantization, insertion, deletion, and combination of the one or more such distortion techniques with random subsequence shuffling. Different distortion techniques in the form of reconstruction of masked input data are provided to solve. The deep learning model performs these different distortion techniques, which force the deep learning model to learn better features. It is to be noted that the system uses a lot of unlabeled data available cheaply as compared to the label or annotated data which is very hard to get.

Abstract: Mislabeled data when used for various applications such as training of Machine Learning (ML) models, can cause erroneous results. The state-of-the-art systems performs the mislabel identification with low confidence, and some require manual intervention. The disclosure herein generally relates to data processing, and, more particularly, to a method and system for identifying mislabeled samples using adversarial attacks. The mislabeled sample may refer to a) a data sample that is tagged with a wrong/incorrect label, or b) a distorted/confusing data sample having similarity with multiple classes. The system performs adversarial attack on training data using varying values of adversarial perturbations, and then identifies, for each of the misguided data samples, least value of adversarial perturbation that was required to misguide each of the data samples. Further, the data samples which were misguided by small values of adversarial perturbation, are identified as candidate mislabeled data samples.

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: 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.