## 1 Preface
[21] proposed a deep learning-based approach for the RUL prediction of rotating parts with big data. With more and more information available to mechanical devices, many new methods have been applied to prediction models. [PERSON] et al. [22] used a physical model to generate health indices whose evolution can be estimated and predicted online. [PERSON] et al. [23] combined the monitoring sensor data and integrated the strengths of the data-driven prognostics approach and the experience-based approach, while reducing their respective limitations. The RUL prediction, based on physical model needs to establish accurate models to describe failure degradation mechanism of parts, while the RUL prediction, based on probability statistics, does not consider the actual working state of different parts, so the application of both methods is limited. With the support of modern information technology and the industrial Internet of Things technology, mechanical and electrical product systems are becoming more and more intelligent, so more and more data on the working status can be obtained, which brings great potential for data driven RUL prediction research [24]. A concrete pump truck is a kind of construction vehicle which uses hydraulic pressure to deliver concrete continuously through the pipeline. A concrete piston, which is located in the conveying cylinder of the pump truck, as shown in Figure 1, is an important part of the concrete pump truck. When the concrete piston is working, it reciprocates in the concrete medium of the conveying cylinder, provides pressure for the concrete, pumps the concrete to a remote place, and plays a sealing role at the same time. The working environment of the concrete piston is very harsh, and it is difficult to establish an accurate failure degradation model and obtain the operating state data directly. At present, there is limited research on the RUL prediction of the concrete piston. By using the condition monitoring data of the concrete pump truck and the replacement information data of the concrete piston, this paper puts forward an RUL prediction method of the concrete piston based on probability statistics and condition monitoring data, and the validity of the method is verified through the result analysis and model application. Figure 2 shows the flowchart of the proposed methodology for RUL prediction. The methodology is divided into two phases: offline and online. In the offline phase, the replacement information data form different concrete pistons are used to fit features based on the Weibull distribution, the condition monitoring data from different concrete pump trucks are used to fit features based on regression algorithm, and the RUL prediction model is built. In the online phase, the RUL of the concrete piston is estimated based on the condition monitoring data from a new concrete pump truck and the real-time working life. The rest of the paper is organized as follows: Section 2 introduces the basic situation of the data. In Section 3, we establish the RUL prediction model of the concrete piston based on probability statistics and data-driven approaches. Section 4 discusses the prediction effect of different regression models, and we use the best prediction model to propose setting the replacement warning point of the concrete piston in Section 5, and conclusions are finally provided. Figure 1: Concrete pump truck and concrete piston. Figure 2: Flowchart of the RUL prediction.