Tata Consultancy Services Limited

Yogesh Tambe

Purushottham Gautham Basavarsu

Trinath Gaduparthi

Himanshu Nirgudkar

Techniques used by the state of the art alarm prediction systems rely mostly on LSTM, which have technical limitation in predicting a sequence with long durations. Embodiments of the present disclosure provide a method and system for long duration alarm sequence predictions from past alarm sequence using a Bi-LSTM and operator sequence recommendations thereof. The BiLSTM with an encoder decoder technique uses true output sequence as input to decoder at each time step during training. This allows the BiLSTM to learn dependencies among input and output sequence effectively. The operator sequence recommended is identified based on closeness of the predicted future output alarm sequence with one among the unique alarm sequences in a mapping table. The alarm sequence closeness is computed using a Matching Sequence Score (MSS) disclosed by the method, since known sequence evaluation metrics such as Blue Score has limitations to be directly applied in alarm sequence evaluation.

Long duration alarm sequence predictions using Bi-LSTM and operator sequence recommendations thereof

This disclosure relates generally to a system and method to estimate an operational risk associated with one or more failures in at least one unit of a process plant. There is a continuous stream of operational data of several variables such as temperature, pressure, etc. Detections are defined in terms of acceptable/unacceptable ranges of parameters over a finite period and operating load of the unit. Often, these predefined parameters must be within a specified range based on operating condition of the process plant and when the measured parameters go beyond, a failure is detected. A risk priority number is estimated from number of occurrences of failure mode, average percentage change from dynamic limits with severity and degree of correlation with detectability from operational data and dynamic limits. Herein, operational risk associated with failure modes can be calculated and updated from time to time automatically from the stream of operational data.

System and method for calculating risk associated with failures in process plants

Currently solutions for early detection of failures in manufacturing utilize predefined threshold levels of the process variables associated with equipment in manufacturing unit/industry plants. The pre-defined threshold and levels thereof are compared with the real values obtained from the manufacturing unit to check behavior of process variables (also referred as 'process parameters') and thus are prone to error. The present disclosure provides systems and method for predicting early warning of operating mode of equipment operating in industry plants which is based on transforming conditions on process parameters into conditions on corresponding fuzzy indices based on their thresholds. The fuzzy indices (concordance index, discordance index) of individual conditions are combined into a composite fuzzy index (composite index or degree of credibility) that describes the failure scenario in the process parameter space. A fuzzy logic-based detection is useful for detecting a failure mode early and providing alerts to operators for necessary action.

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Pune, India

Yogesh Tambe