Remaining useful life (RUL) prediction in real operating environment (ROE) plays an important role in condition-based maintenance. However, the life information in ROE is limited, especially for some long-life products. In such cases, accelerated degradation test (ADT) is an effective method to collect data and then the accelerated degradation data are converted to normal level of accelerated stresses through acceleration factors. However, the stresses in ROE are different from normal stresses since there are some other stresses except normal stresses, which cannot be accelerated, but still have impact on the degradation. To predict the RUL in ROE, a nonlinear Wiener degradation model is proposed based on failure mechanism invariant principle which is the precondition and requirement of an ADT and a calibration factor is introduced to calibrate the difference between ROE and normal stresses. Moreover, the unit-to-unit variability is considered in the concern model. Based upon the proposed approach, the RUL distribution is derived in closed form. The unknown parameters in the model are obtained by a new two-step method through fuzing converted degradation data in normal stresses and degradation data in ROE. Finally, the validity of the proposed model is demonstrated through several simulation data and a case study.

实际操作环境（ROE）中的剩余使用寿命（RUL）预测在基于状态的维护中起着重要作用。但是，ROE中的寿命信息是有限的，特别是对于某些长寿命产品。在这种情况下，加速退化测试（ADT）是一种有效的数据收集方法，然后通过加速因子将加速退化数据转换为正常水平的加速应力。但是，ROE中的应力不同于正应力，因为除了正应力之外还有其他一些应力，这些应力无法加速，但仍会对降解产生影响。要预测ROE中的RUL，提出了基于失效机制不变原理的非线性维纳退化模型，该模型是ADT的前提和要求，并引入了一个校正因子来校正ROE和正应力之间的差异。此外，在关注模型中考虑了单位间的可变性。基于建议的方法，RUL分布以封闭形式导出。该模型中的未知参数是通过一种新的两步法，通过融合正应力中的转换后退化数据和ROE中的退化数据而获得的。最后，通过一些仿真数据和案例研究证明了所提出模型的有效性。RUL分布以封闭形式导出。该模型中的未知参数是通过一种新的两步法，通过融合正应力中的转换后退化数据和ROE中的退化数据而获得的。最后，通过一些仿真数据和案例研究证明了所提出模型的有效性。RUL分布以封闭形式导出。该模型中的未知参数是通过一种新的两步法，通过融合正应力中的转换后退化数据和ROE中的退化数据而获得的。最后，通过一些仿真数据和案例研究证明了所提出模型的有效性。