# An Adaptive Process Monitoring Method Based on an Improved ADALINE Neural Network
###### Abstract
The ADALINE neural network is an artificial neural network algorithm that is mainly used in the field of adaptive control. [PERSON] et al. proposed a coordination control technology based on ADALINE and achieved a reduction in battery capacity [31]. [PERSON] et al. used ADALINE to extract the harmonics of signals and detect faults in rotating machines, and the results showed that the new method reduced the processing time for wireless sensor networks [32]. Although the ADALINE neural network has developed rapidly, it has not been applied to machining condition monitoring. Considering its adaptive adjustment capability, ADALINE can be used to construct the control limit adjustment in the adaptive monitoring model proposed in this paper. ADALINE has the advantages of low computational complexity and high processing efficiency, even when dealing with a large amount of data, although its learning ability may be weaker than that of more complex and highly fitting advanced neural networks. In large-scale production or real-time monitoring scenarios, continuous collection and processing of a large amount of data pose high requirements for the computing and storage capabilities of the data processing system. However, for fixed production lines, it can handle data well and achieve good results. Based on the above considerations, this paper proposes a new adaptive process monitoring method based on an improved ADALINE neural network. Its novelty lies in that, first, its architecture adopts an adaptively learnable threshold and an adaptive monitoring and control strategy, which is carried out in a way of non-fixed sampling intervals and non-fixed sample sizes, fully absorbing the strategic advantages of the VSS control chart and the VP control chart. At the same time, it takes into account the improvement of control ability and quality trend adjustment through knowledge accumulation on the production line. That is, the boundary for judging product quality is continuously optimized dynamically, enabling it to change dynamically with the gradual change in the process, timely reflecting the actual state of the process and being more conducive to improving product quality. Second, the use of the ADALINE neural network method has the advantages of low computational complexity and high processing efficiency even with a large amount of data, enabling a timelier response. Moreover, as a neural network algorithm, it can fully consider the cumulative effect of data as the production line progresses, making it very suitable for the mass production process on the production line. Efficient preprocessing methods and adaptive monitoring strategies are also considered in this paper to improve the ability of the condition monitoring approach. For the wavelet threshold denoising method [33; 34; 35; 36; 37; 38], the traditional method usually relies on experience to select decomposition layers, which restricts the effectiveness of noise reduction. After consulting the literature, we have found that the modulus maxima of the wavelet coefficients of useful signals and noise have different propagation characteristics. Based on this feature, we improve the denoising method. Moreover, we exploit the advantages of the VSS control chart and VP control chart [39; 40], considering the quality characteristics changes during the monitoring process and designing adaptive monitoring strategies to improve monitoring efficiency [41]. The remainder of this paper is organized as follows. Section 2 briefly introduces the theory and methodology used in this paper, including the maximum modulus of wavelet transforms and the ADALINE neural network. In Section 3, the framework of the adaptive process monitoring method is proposed. This framework mainly includes preprocessing the data, establishing an adaptive monitoring strategy, and adaptively adjusting the control limit. In advanced manufacturing fields such as aerospace and microelectronics, high-precision parts are needed.