Fuzzy Logic Based Adaptive Parameter Estimation System in Moving Measurement Systems

Fast and accurate weighing of the weighing systems used in industrial filling systems is of great importance in terms of increasing production capacity and maintaining product quality. In facilities that grind and process grain, machines and equipment are positioned horizontally and vertically on steel structures. Since these machines continuously perform grinding, transferring, filling, and emptying operations, they create continuous vibration in the mechanical systems they are connected to. Moving weighing systems are significantly affected by these mechanical systems. When the impact effect of pneumatic valves-controlled covers in moving weighing systems is added to these structural mechanical vibrations, there are significant waits and delays in weighing systems that measure performance. For this reason, in a performance measurement system in a flour mill, the measurement interval increases as the amount of weighing increases. For example, in a moving weighing system that performs 50 kg performance weighing, the measurement interval can increase up to 15 seconds, which is quite long. In this study, an applied study has been conducted to increase the weighing performance in moving weighing systems and to minimize the measurement interval. The data collection process in the study focuses on two main components: load cell data and IMU data. Thus, it is aimed to overcome the difficulties of traditional methods used in weighing systems, which are generally observed to be insufficient to combat slow and noisy data. The analysis techniques used in this study are Kalman Filtering, Dynamic Q and R Matrix Updates, Comparative Analysis and Statistical Analysis. The Kalman filter was used for the integration of Load cell and IMU data and was applied to filter out noise and oscillations in the weighing data and make more accurate weight estimates. The results obtained showed that the dynamic Kalman filtering method can provide faster and more accurate weighing results compared to traditional methods, with error rates varying between 0.4% and 1% for different combinations of Q and R values in measurements made on the scale. Dynamic Kalman filtering method effectively filters oscillatory and noisy load cell signals, with error rates of 0.7% to 1% for Q=0.02 and R=17 parameters, and error rates of 0.4% to 0.7% for Q=0.07 and R=13 parameters. was able to obtain more accurate weight estimates. This study has shown that the dynamic Kalman filtering method is a potential method that can be used in industrial filling systems. This method can contribute to increasing production capacity and maintaining product quality by providing faster and more accurate weighing results. In this respect, the research has a unique contribution. This method provides a revolutionary development in industrial weighing systems and fills an important gap in the literature.