Data-Driven Fault Diagnostics for Industrial Processes: An Application to Penicillin Fermentation Process

We consider the problem of fault detection and isolation for the penicillin fermentation process. A penicillin fermentation process is a highly complex and nonlinear dynamic process with batch processing. A data-driven approach is utilized for fault diagnostics due to the availability of huge batch processing data and the unavailability of an analytical model. To address the non-linearity, a subspace-aided parity-based residual generation technique is proposed by using a just-in-time learning approach. For the just-in-time learning approach, the most similar data samples are selected from the database for incoming test samples and a subspace aided parity-based residual is generated using these samples. The designed fault detection technique is implemented for the penicillin fermentation process to demonstrate real-time health monitoring of the process under sensor noise and process disturbances. Two sensor faults and an actuator fault are considered and successfully detected using the proposed technique. Further, a fault isolation approach is developed to isolate these faults and their location has been identified. A case study is given to show the improvement offered by the proposed technique for the fault detection rate and minimization of the false alarm rate as compared to the existing techniques for the penicillin fermentation process.