Zhu et al.  proposed a model based on principal component analysis and a neural network for the multi-fault diagnosis of sensor systems. By means of data fusion [3�C5], different sources of information are combined to improve the performances of the system. Fusion may be useful for several objectives such as detection, recognition, identification, tracking, change detection, decision making, etc. These objectives may be encountered in many application domains such as defense, robotics, medicine, electrochemistry, etc. Kewley  introduced the notion that in data fusion the simple form is data + algorithms + knowledge equal to data fusion. Xie and Quan  reported that the data fusion method could be applied in the fault diagnosis field.
The faults are diagnosed through three levels which are data fusion level, feature level and decision level respectively. Nassar and Kanaan  surveyed and discussed the state-of-the-art studies related to the factors affecting the performance of data fusion algorithms, and have integrated data fusion performance research findings. Xue  presented a fault diagnosis system based on multi-sensor data fusion algorithm, which is composed of a local data fusion level and whole data fusion level. In order to measure a physical quantity, a sensor is defined as a measuring device that exhibits a characteristic of an electrical nature (such as charge, voltage and current). In electrochemical measurements this consists of a prepared sensing device as a working electrode, and a reference electrode.
These electrodes Carfilzomib are enclosed in the sensor housing in contact with a liquid electrolyte. The measured pH value is a very important parameter in many fields, such as wastewater monitoring, clinical diagnosis and culture. The pH sensor array fabricated by using ruthenium dioxide thin film with sputtering has been investigated . Zhang et al.  introduced several kinds of methods for sensor fault diagnosis technology. Xu et al.  proposed a method of sensor fault diagnosis based on the least squares support vector machine online prediction.This paper utilizes a fault diagnosis method and integrates some data fusion algorithms to apply them to a pH sensor array. We used the fault diagnosis to obtain the coefficients of the confidence matrix and to judge faulty sensors, and then the measured pH data of the faulty sensor are eliminated. The measured pH data of the other sensors are then used in the average, self-adaptive and coefficient of variance data fusion methods to perform data fusion. The pre-processing of the measured data before data fusion can increase the reliability of pH measurement results.