Root cause identification (RCI) of faults in industrial processes enables plant operators to pinpoint the source(s) of the fault and take appropriate corrective actions to prevent failures. Conventional techniques for RCI are not particularly suited for causal maps having cycles and time lags that are characteristic of industrial operations. We propose a fault traversal and root cause identification (FTRCI) algorithm for automatic identification of fault traversal pathways and root cause variables from causal maps. Multivariate time delayed transfer entropy between fault variable states is calculated to model the causal dependencies that are represented as a causal map. Then, using FTRCI, the fault traversal paths within the causal map are traced and the corresponding root cause variables are identified. The proposed methodology was applied to complex faults in the Tennessee Eastman process and an industrial coal pulverizer, and the root cause variables identified using this methodology were in accordance with the process knowledge.

Real-time fault detection, classification and diagnosis in manufacturing and process industries is essential to prevent unplanned downtime and improve the reliability of industrial operations. While several well-accepted machine learning techniques exist for fault detection and classification, there is a need for a reliable and generalized fault diagnosis technique that identifies sensors responsible for industrial faults in real time. In this work, we propose a variable perturbation matrix-based method for fault diagnosis in industrial processes. We utilize the Long ShortTerm Memory for prediction due to its ability to memorize temporal information in time-series data. First, the fault is detected, then one or more independent variables are perturbed across the fault detection point to check the sensitivity of the diagnosis model for the corresponding variables. Thus, a perturbation matrix is calculated and variables with high sensitivity are selected as the variables responsible for fault. The proposed method is applied to an Industry 4.0 quality control test bed set up for electronic components, the dataset for which is provided in the Prognostics and Health Management Euorpe-21 (PHME-21) data challenge. The proposed method accurately detected and classified 6 faults in the test bed and correctly diagnosed the most significant variables. Due to high fault detection accuracy coupled with sensitivity-based fault diagnosis, the method is suitable for multivariate industrial systems.

Real-time root cause identification (RCI) of faults or abnormal events in industries gives plant personnel the opportunity to address the faults before they progress and lead to failure. RCI in industrial systems must deal with their complex behavior, variable interactions, corrective actions of control systems and variability in faulty behavior. Bayesian networks (BNs) is a data-driven graph-based method that utilizes multivariate sensor data generated during industrial operations for RCI. Bayesian networks, however, require data discretization if data contains both discrete and continuous variables. Traditional discretization techniques such as equal width (EW) or equal frequency (EF) discretization result in loss of dynamic information and often lead to erroneous RCI. To deal with this limitation, we propose the use of a dynamic discretization technique called Bayesian Blocks (BB) which adapts the bin sizes based on the properties of data itself. In this work, we compare the effectiveness of three discretization techniques, namely EW, EF and BB coupled with Bayesian Networks on generation of fault propagation (causal) maps and root cause identification in complex industrial systems. We demonstrate the performance of the three methods on the industrial benchmark Tennessee-Eastman (TE) process.  For two complex faults in the TE process, the BB with BN method successfully diagnosed correct root causes of the faults, and reduced redundancy (up to 50%) and improved the propagation paths in causal maps compared to other two techniques.