Sensor systems have been used widely in many fields. However, sensors are prone to faults, which greatly reduce the performance of the trained pattern-recognition model. To improve the reliability and stability of the sensor system, it is essential to apply prognostics and health management to the sensor system. A novel health-prediction model of the sensor system is established based on the unascertained deep soft sensor (UDSS) and relevant vector-machine ensemble (RVME). The first step in health prediction is to extract the performance variables. Based on unascertained mathematics and the deep belief network, a novel UDSS is proposed to extract the performance variables, which are called the health reliability degree (HRD). The HRD is applied as the input of health prediction. The second step is to establish an appropriate predictor. Bagging is used as the framework, the relevant vector machine is used as the weak learner, and RVME is utilized to structure continuous single-step or multiple-step health predictions. To verify the effectiveness, the proposed method is applied to a gas-sensor system. An experimental gas-monitoring system is designed and developed to obtain sufficient experimental data. The simulation result demonstrates that compared to other methods, the proposed method has a lower average relative error of 0.60%.

传感器系统已广泛应用于许多领域。但是，传感器容易出现故障，从而大大降低了训练后的模式识别模型的性能。为了提高传感器系统的可靠性和稳定性，必须对传感器系统应用预测和健康管理。基于未确定的深层软传感器（UDSS）和相关的矢量机集成（RVME），建立了传感器系统的健康预测模型。健康预测的第一步是提取性能变量。基于不确定的数学和深度信念网络，提出了一种新的UDSS来提取性能变量，称为健康可靠性度（HRD）。HRD被用作健康预测的输入。第二步是建立适当的预测变量。使用Bagging作为框架，使用相关的矢量机作为弱学习器，使用RVME构建连续的单步或多步健康预测。为了验证有效性，将所提出的方法应用于气体传感器系统。设计并开发了一个实验气体监测系统，以获得足够的实验数据。仿真结果表明，与其他方法相比，该方法的平均相对误差较低，为0.60％。设计并开发了一个实验气体监测系统，以获得足够的实验数据。仿真结果表明，与其他方法相比，该方法的平均相对误差较低，为0.60％。设计并开发了一个实验气体监测系统，以获得足够的实验数据。仿真结果表明，与其他方法相比，该方法的平均相对误差较低，为0.60％。