## 1 Introduction  
Industrial level sensors are employed as standalone devices (e.g., to measure temperature and humidity independently), packed modules (e.g., to measure temperature and humidity synchronously), or even bundled in target systems. With the rapid development of the Internet of Things, the production and procurement costs of sensors effectively satisfy the economic requirements of customers. In addition, multiple sensors can be configured in existing and single structures [1]. Daily collected data are useful for enterprises, particularly for meeting set objectives, i.e., preventive maintenance plans. Compared with sensors used in social sciences and medical fields, industrial sensors generate more missing data during data collection. The occurrences of missing data in industrial applications are primarily due to system updates and unequal radio-frequency periods, which are expected situations. In real applications, determining the approaches of addressing missing data is a common challenge prior to implementing meaningful models for decisions. Generally, analysts determine possible reasons for missing data based on their industry background and work experience, and then take appropriate steps to address them. This study considers the following two general scenarios: data are missing completely at random or missing at random [2, 3]. Appropriate strategies for dealing with these scenarios are to either ignore missing data or impute meaningful values [2; 4]. However, it is difficult to establish whether data are missing with randomness when they are sourced from hundreds or even thousands of sensors. For example, Scania, the world's leading provider of transport solutions, has proposed a viable maintenance plan based on budget constraints for their global customers [5; 6]. Not all predefined data fields can be collected because some fields may not be permitted due to the purchased maintenance plan. In such circumstances, it is more appropriate to treat missing data as non-existing or of a separate type of natural event. Particularly, the constantly generated unstructured sensor data, which may likely contain many predictors to possible failures. Thus, it is a daunting task to collect, store, and analyze all such data [7]. Otherwise, missing data can be presented in both the training and test sets, only the training set, only the test set, or neither sets. To the best of our knowledge, the distribution consistency of training and test sets is not discussed frequently in the literature related to missing data manipulation. Class imbalance is another factor that impacts the results of a classification model. [PERSON] et al. [8] stated that class imbalance hinders the performance of classifiers owing to their accuracy-oriented design, which typically results in the minority class being overlooked. Using a benchmark dataset from the KEEL (Knowledge Extraction based on Evolutionary Learning) repository, the authors proposed the imbalance ratio (IR) for identifying the degree of class imbalance (\(1.5\leq\text{IR}<9\): moderately imbalanced; IR \(\geq 9\): highly imbalanced). Class imbalance problems occur frequently in many applications in various fields, and there are two general approaches to deal with this problem that satisfy different requirements. One approach is to apply a resampling (under- or over-sampling) method to achieve a balance between the majority and minority classes. The other approach is to apply a weighting method assigning different weights to the majority and minority classes. Additionally, then, the examples from different classes are in equal status. Whichever approach is used, the original data distribution would change. Different combinations of the three factors (missing data, consistency of their distribution across the training and test sets, and class imbalance) can generate many different scenarios.