摘要：AbstractIn the Biotechnology 4.0 paradigm, process analytical technology (PAT) tools are being increasingly deployed in biomanufacturing to gain improved process insights through extensive use of advanced and automated sensing techniques. Critical parameters, such as pH, dissolved oxygen (DO), temperature, and metabolite concentrations, are routinely measured and controlled in a cell culture process. While these extensive networks of sensors generate critical process information and insights, they are also prone to failures and malfunctions. In this paper, we propose a condition-based maintenance (CbM) framework for real-time sensor-health management, with a focus on fault detection, diagnosis, and prognostics. To this effect, a slow-feature analysis (SFA)-based platform is proposed for the detection and diagnosis of sensor-health. For health prognostics, a Gaussian process (GP) model is proposed for forecasting the remaining useful life (RUL) of the sensor along with the probability of failure. The efficacy of the proposed sensor-heath management strategy is demonstrated in a biomanufacturing process.
