This study proposes Dynamic Warping (DW) as a sensor reconstruction method for Remaining Useful Life (RUL) estimation. The method utilizes the DW model for sensor reconstruction, where Median Absolute Deviation measures the reconstruction error, which is expected to increase when abnormal system behavior is measured. We apply an exponential model to the reconstruction error to estimate a system’s RUL. The DW model is based on the Dynamic Time Warping algorithm applied to a non-temporal context. The concept is to preprocess sensor data into a non-temporal motion profile representing a cycle. We validate our proposed DW model with two baseline models: Singular Value Decomposition (SVD) and LSTM Autoencoder (LSTM-AE). The SVD model is applied to the non-temporal motion profile, while the LSTM-AE model is applied to the original sensor data. A case study was conducted at a semiconductor Original Equipment Manufacturer, whose dataset contained information on a bearing failure in a water-cooled direct drive rotary motor. The failure occurred due to increased friction caused by bearing wear. The valuable motion control signal found was torque applied to the shaft for the R and S phases. It was demonstrated that the proposed method is most efficient, and an alarm can be raised 11 hours before failure, after which the RUL can be estimated, which is promising for warning service engineers for this industrial application. This research shows that the DW model could predict maintenance furthest in advance while only needing a fraction of the training data.

Abstract Just-in-time defect prediction (JITDP) research is increasingly focused on program changes instead of complete program modules within the context of continuous integration and continuous testing paradigm. Traditional machine learning-based defect prediction models have been built since the early 2000s, and recently, deep learning-based models have been designed and implemented. While deep learning (DL) algorithms can provide state-of-the-art performance in many application domains, they should be carefully selected and designed for a software engineering problem. In this research, we evaluate the performance of traditional machine learning algorithms and data sampling techniques for JITDP problems and compare the model performance with the performance of a DL-based prediction model. Experimental results demonstrated that DL algorithms leveraging sampling methods perform significantly worse than the decision tree-based ensemble method. The XGBoost-based model appears to be 116 times faster than the multilayer perceptron-based (MLP) prediction model. This study indicates that DL-based models are not always the optimal solution for software defect prediction, and thus, shallow, traditional machine learning can be preferred because of better performance in terms of accuracy and time parameters.

Predictive maintenance is a technique for creating a more sustainable, safe, and profitable industry. One of the key challenges for creating predictive maintenance systems is the lack of failure data, as the machine is frequently repaired before failure. Digital Twins provide a real-time representation of the physical machine and generate data, such as asset degradation, which the predictive maintenance algorithm can use. Since 2018, scientific literature on the utilization of Digital Twins for predictive maintenance has accelerated, indicating the need for a thorough review. This research aims to gather and synthesize the studies that focus on predictive maintenance using Digital Twins to pave the way for further research. A systematic literature review (SLR) using an active learning tool is conducted on published primary studies on predictive maintenance using Digital Twins, in which 42 primary studies have been analyzed. This SLR identifies several aspects of predictive maintenance using Digital Twins, including the objectives, application domains, Digital Twin platforms, Digital Twin representation types, approaches, abstraction levels, design patterns, communication protocols, twinning parameters, and challenges and solution directions. These results contribute to a Software Engineering approach for developing predictive maintenance using Digital Twins in academics and the industry. This study is the first SLR in predictive maintenance using Digital Twins. We answer key questions for designing a successful predictive maintenance model leveraging Digital Twins. We found that to this day, computational burden, data variety, and complexity of models, assets, or components are the key challenges in designing these models.

The systematic literature review (SLR) process includes several steps to collect secondary data and analyze it to answer research questions. In this context, the document retrieval and primary study selection steps are heavily intertwined and known for their repetitiveness, high human workload, and difficulty identifying all relevant literature. This study aims to reduce human workload and error of the document retrieval and primary study selection processes using a decision support system (DSS). An open-source DSS is proposed that supports the document retrieval step, dataset preprocessing, and citation classification. The DSS is domain-independent, as it has proven to carefully select an article’s relevance based solely on the title and abstract. These features can be consistently retrieved from scientific database APIs. Additionally, the DSS is designed to run in the cloud without any required programming knowledge for reviewers. A Multi-Channel CNN architecture is implemented to support the citation screening process. With the provided DSS, reviewers can fill in their search strategy and manually label only a subset of the citations. The remaining unlabeled citations are automatically classified and sorted based on probability. It was shown that for four out of five review datasets, the DSS's use achieved significant workload savings of at least 10%. The cross-validation results show that the system provides consistent results up to 88.3% of work saved during citation screening. In two cases, our model yielded a better performance over the benchmark review datasets. As such, the proposed approach can assist the development of systematic literature reviews independent of the domain. The proposed DSS is effective and can substantially decrease the document retrieval and citation screening steps' workload and error rate.