The boiler is an essential energy conversion facility in a thermal power plant. One small malfunction or abnormal event will bring huge economic loss and casualties. Accurate and timely detection of abnormal events in boilers is crucial for the safe and economical operation of complex thermal power plants. Data-driven fault diagnosis methods based on statistical process monitoring technology have prevailed in thermal power plants, whereas the false alarm rates of those methods are relatively high. To work around this, this paper proposes a novel fault detection and identification method for furnace negative pressure system based on canonical variable analysis (CVA) and eXtreme Gradient Boosting improved by genetic algorithms (GA-XGBoost). First, CVA is used to reduce the data redundancy and construct the canonical residuals to measure the prediction ability of the state variables. Then, the fault detection model based on GA-XGBoost is schemed using the constructed canonical residual variables. Specially, GA is introduced to determine the optimal hyperparameters of XGBoost and speed up the convergence. Next, this paper presents a novel fault identification method based on the reconstructed contribution statistics, considering the contribution of state space, residual space and canonical residual space. Besides, the proposed statistics renders different weights to the state vectors, the residual vectors and the canonical residual vectors to improve the sensitivity of faulty variables. Finally, the real industrial data from a boiler furnace negative pressure system of a certain thermal power plant is used to demonstrate the ability of the proposed method. The result demonstrates that this method is accurate and efficient to detect and identify the faults of a true boiler.

Exponential weighted moving average (EWMA) run-to-run (RtR) controller is a widely used controller in semiconductor manufacturing process. Machine aging, model-plant mismatch and other factors lead to system performance degradation of EWMA RtR control system, wafer yield reduction, and even the safety of the manufacturing pr ocess degradation. A performance monitoring method for EWMA RtR control system is proposed based on improved KL divergence. First, a novel control performance index (CPI) is constructed based on improved KL divergence only requiring routine operation data. Then, the control limit of the proposed CPI is obtained by kernel density estimation (KDE) method. Next, the control performance online monitoring method is provided to monitor the performance of EWMA RtR control system. Finally, numerical simulations of chemical mechanical polishing (CMP) process are given to illustrate the efficiency of the proposed method.