Automatic sensor fault detection and sensor reconstruction algorithm for emergency recovery in industrial fields

Owing to advances in information technology, some studies have been conducted on work environment monitoring system using various sensors in order to prevention of accidents, information gathering, and optimal management of work environment. It was found that a factory environment monitoring system using climate sensors such as O2, Co2, Nh3, and Pm2 has recently become essential because it can help protect the safety and health of workers. Climate sensors are a mesh-type arrangement placed at certain distances apart to acquire and anlyze exact environmental information. Sensitivity, specificity, and accuracy increase as the number of sensors are increased. However, as the number of sensors increases, it becomes more difficult to detect faulty sensors, and in the worst case, false information can lead to accidents. It is necessary, therefore, for environment monitoring systems using a large number of climate sensors to have a function that will automatically detect the failure of a sensor. The value of an individual climate sensor is organically realted to the value of neighbor sensors, unless they are located in enclosed spaces. If the sensor value at a specific position changes, the neighboring sensor's values are also changed. In the past, much research has studied algorithms to improve the sensing accuracy of specific location using neighbor sensor data based on these principle. If these algorithms are used inversely, it is possible to infer or predict the environmental information in the area where the sensor has failed, by using the values from neighboring sensors. Even with these systems in operaton, a major concern on many industrial sites is that work does not stop even if a sensor failure is detected. In other words, when workers are in areas where a sensor has failed, they may become exposed to hazardous conditions. Therefore, even if a sensor fails, for the sake of the workers, it is necessary to continuously provide environmental information to those in the affected area. This paper presents the automatic sensor fault detection and sensor reconstruction algorithm for emergency recovery relative to the production of continuous and reliable environmental data. The principle of automatic sensor fault detection and the sensor reconstruction algorithm are the same. The proposed algorithms consist of four steps. In the first step, a total of nine sensors consisting of 3∗3 are configured as one set. In the second step, the three sensors, including the central sensor, are grouped into one group. One set becomes a total of four groups. In the third step, reference curve maps (RCM) are created to record changes in sensor values according to the amount of ambient gas. The RCM records the sensor's values as the gas changes in density. Four RCMs are created per set. A total of 32 RCMs are created because one sensor is included in a total of eight sets. In the fourth step, the automatic sensor fault detection and sensor reconstruction algorithms are performed. Automatic sensor fault detection consists of two substeps. In the first sub-step, the comparative values of the failed sensor and the neighboring sensor are added to the RCM. In the second substep, if more than half of the comparison result deviates from the normal range, the target sensor is judged to be faulty, and the supervisor is notified. If the superviisor determines that the sensor is normal, the RCMs are then updated to improve accuracy. Sensor reconstruction is performed when the sensor is determined to be faulty. Sensor reconstruction consists of two further substeps. First, the failure of the sensor is inferred using all RCMs and the linear interpolation technique. Second, the final value of the failed sensor is determined by using its predicted value, as obtained by using RCMs and weighted averages.