Sensor fault detection and diagnosis (FDD) has great significance for ensuring the energy saving and normal operation of the air conditioning system. Chiller systems serving as an important part of central air conditioning systems are the major energy consumer in commercial and industrial buildings. In order to ensure the normal operation of the chiller system, virtual sensors have been proposed to detect and diagnose sensor faults. However, the performance of virtual sensors could be easily impacted by abnormal data. To solve this problem, virtual sensors combined with the maximal information coefficient (MIC) and a long short-term memory (LSTM) network is proposed for chiller sensor fault diagnosis. Firstly, MIC, which has the ability to quantify the degree of relevance in a data set, is applied to examine all potentially interesting relationships between sensors. Subsequently, sensors with high correlation are divided into several groups by the grouping thresholds. Two virtual sensors, which are constructed in each group by LSTM with different input sensors and corresponding to the same physical sensor, could have the ability to predict the value of physical sensors. High correlation sensors in each group improve the fitting effect of virtual sensors. Finally, sensor faults can be diagnosed by the absolute deviation which is generated by comparing the virtual sensors’ output with the actual value measured from the air-cooled chiller. The performance of the proposed method is evaluated by using a real data set. Experimental results indicate that virtual sensors can be well constructed and the proposed method achieves a significant performance along with a low false alarm rate.

In this work, fault detection and isolation (FDI) of industrial automation systems with a closed-loop configuration is under consideration. Specifically, the mean of the input and output vectors is time-varying with the variation of the reference vectors. This brings a great challenge to the existing multivariate analysis-based methods, which are lack of consideration of closed-loop dynamics. To this end, a stable image representation (SIR)-aided dynamic canonical correlation analysis (SD-CCA)-based FDI method is proposed. In this method, residual generation is performed in two steps. Residual vectors of the closed-loop dynamic are first generated based on the identified data-driven SIR to remove the time-varying mean. Then, an SD-CCA-based residual generator is established, which enhances the fault detectability by considering the correlation between zero-mean input and output. Finally, by maximizing the fault direction angle, an optimal fault isolation method based on the fault direction angle of SD-CCA is proposed. It is followed by a sensitivity analysis of the proposed method, furthermore, whose performance is evaluated by comparing with several state-of-the-art methods on a numerical simulation and a real chiller system. Results show that the proposed method has a better FDI performance than the compared methods.

In this article, a correlation-aided neural network (CANN) framework is proposed for distributed process monitoring of large-scale industrial automation systems. First, to model the nonlinear data relationship of multiple subsystems, a feature space is learned for each subsystem by means of neural network-based nonlinear projection. Second, different from conventional distributed monitoring methods, a distributed learning method and a novel objective function are proposed, where the influence of uncertainties is decreased by considering the correlation of variables among multiple subsystems. Third, a process monitoring method is proposed based on the CANN framework. The impact of the fault is analyzed, and the optimal monitoring performance is achieved by considering the correlation of all subsystems. To further reduce communication overhead among multiple subsystems, a tradeoff between monitoring performance and communication overhead is made based on gradients of the CANN model. Theoretical analysis demonstrates the superiority of the proposed method. The case studies on a multizone heating, ventilation, and air-conditioning system and the Tennessee Eastman benchmark process are given to evaluate and compare the effectiveness of the proposed method.

Aiming at the precise positioning problem of cigarette filter box cartoning machine in the process of pushing the production line, a new type of push mechanism control system is designed. The fuzzy control theory is combined with the PID controller, and the PID parameters are adaptively adjusted by fuzzy control. It can improve system response speed and control accuracy. The results show that the improved push mechanism can realize the flexible filter rod packing, and the positioning accuracy is better than 0.1mm, which improves the packing quality of the filter box cartoning machine.