## 1 Introduction  
Due to time constraints and cost, close attention has been paid to acquiring reliable information from field data with minimal effort, especially those products with high reliability and longevity. Accelerated life test (ALT) is an effective mechanistic modeling approach that collects more information about reliability and life in a shorter time by conducting a more rigorous test environment than in normal operational conditions. However, when dealing with highly reliable components or systems, even for Prognostic and Health Management (PHM) applications, few failed data exist and ALT may not be sufficient to acquire life and prognosis results within the available time frame. Given that performance data can be monitored either with or without failure, degradation data could offer more reliability analysis compared to ALT, including providing more reliable information and more credible reliability estimation, and the basis for stronger extrapolation and prognosis estimation, in which cases using degradation modeling and analysis with ADT may be more appropriate. ADT is extensively used to obtain the quality characteristic information, whose degradation data are related to life because degradation is the accumulation of product damage overtime [1]. Based on the different stress loading methods, ADT can be divided into CSADT, step-stress ADT (SSADT), and progressive-stress ADT (PSADT). CSADT has a widespread application in many fields, such as aerospace, engineering, and industry due to its convenient operation. For ADT, there are two phases to model. Firstly, we need to depict the relationship between the degradation parameters. Secondly, we can predict when the measured unit will reach the predefined failure threshold level to meet the failure criteria. There are many kinds of methods to model accelerated degradation data, such as the general path model and stochastic process model, and so on. The general path model was first introduced by [PERSON] and [PERSON] [2] in 1993, with its failure time determined by known stochastic parameters. However, it might not be intended to characterize the inherent randomness of products and dynamics due to unobserved environmental factors. The stochastic process models widely applied in ADT modeling are the Gamma process, Wiener process, and Inverse Gaussian process [3]. In contrast to Gamma processes and Inverse Gaussian processes suitable only for the modeling of monotone degradation data, Wiener processes characterize the dynamic properties of the products well due to their non-monotonic properties, infinite separability, and physical interpretation. Based on these typical properties, the Wiener process had been used intensively to model the degradation processes, taking LEDs [4; 5; 6], laser generators [7], rotating element bearings [8], capacitors [9], and lithium batteries [10] as examples. In the linear ADT analysis, based on Brownian motion and Gamma process, [PERSON] and [PERSON] [11] presented two models of accelerated degradation and analyzed CSADT information on carbon-film resistors under temperature stresses. [PERSON], et al. [12] modeled the degradation process using a linear Wiener process and evaluated the reliability of products utilizing the multi-fusion information. [PERSON] and [PERSON] [13] used a Wiener process with a linear drift to obtain the field reliability and used it for LED CASDT data on ambient temperature and electric current under the multiple acceleration conditions. In reality, there are more degradation processes of products experiencing uncertainties such as random effects, measurement errors, and nonlinearity. For the nonlinear ADT modeling, [PERSON] and [PERSON] [14] proposed a model based on the absolute temperature reciprocal Wiener process model with a time-scale transformation for cable CSADT data analysis. Given the variation among units, [PERSON], et al.