# An Adaptive Process Monitoring Method Based on an Improved ADALINE Neural Network
###### Abstract  
In the manufacturing of parts, the machining process is a critical factor for ensuring product quality. Process condition monitoring is essential for this process. Traditional monitoring approaches that usually use static thresholds are inefficient and lag behind process monitoring, which limits the regulation capability of process monitoring. In this paper, a new adaptive process monitoring method based on process state information is proposed. More precisely, two critical issues are addressed. First, adaptive control limits improve the dynamic monitoring ability. Second, designing monitoring strategies for quality trends improves the monitoring efficiency. In this paper, a framework for an adaptive process monitoring method is designed. First, the modulus maxima is calculated to improve the preprocessing method of wavelet threshold denoising. Second, an adaptive monitoring strategy is designed for different quality trends to improve the monitoring efficiency. Third, the real-time adaptive update of the control limits is realized for machining condition monitoring based on the artificial adaptive linear element (ADALINE) neural network. Finally, experiments are conducted to validate the feasibility of the proposed approach, and the results show that the proposed approach has good applicability as needed in practice. process monitoring; ADALINE neural network; adaptive control; fault diagnosis; machining process 2020  
Over the years, intensive research has been conducted in the field of fault diagnosis to eliminate faults [5; 6; 7], and great progress has been made in the field. The status monitoring method is also part of fault diagnosis technology [8; 9]. The process monitoring method is currently the main way to identify and eliminate an abnormal state in a machining process. There have been many studies on process monitoring. [PERSON] et al. described and discussed fast wafer-level reliability (fWLR) monitoring as a supporting procedure on productive wafers to meet the stringent quality requirements in automotive, medical, and aviation applications [10]. [PERSON] et al. investigated the oscillation behavior of tools and joining members during ultrasonic metal welding (USMW) under different manufacturing conditions by monitoring external sensor data of structure-borne sound [11]. [PERSON] et al. stacked a Fisher autoencoder (FAE) to build a deep FAE (DFAE) and then combined the DFAE with a self-organizing map (DFAE-SOM) for visual process monitoring to improve the accuracy of process monitoring [12]. [PERSON] et al. used pattern recognition and random deep forest approaches to carry out status monitoring on machining conditions and realize abnormal status identification [13]. We find that process monitoring effectively improves the reliability and safety of industrial production systems and reduces accident risk [14]. However, existing process monitoring approaches are usually performed by comparing the characteristics of the monitoring data. Generally, when the monitoring index exceeds the maximum value of the normal range, we believe that a fault has occurred [15; 16; 17]. The boundary of the normal range is the control limit, which is essential for machining process monitoring. Traditional process monitoring methods focus on improving the performance of classifiers, that is, considering how to more accurately distinguish between good and bad products. However, their drawback lies in the fact that the boundary (the control limit) is still static for judging product quality [18; 19; 20; 21; 22; 23]. This type of condition monitoring method conducts production process monitoring by using fixed thresholds, fixed sampling intervals, and fixed sample sizes. Its advantages include simple design and ease of operation. Nevertheless, modern production processes often feature dynamism and quality drift. In actual production processes, the effectiveness of quality monitoring and the economy of control are the primary concerns of designers.