Predicting failures and maintenance time in predictive maintenance is challenging due to the scarcity of comprehensive real-world datasets, and among those available, few are of time series format. This paper introduces a real-world, multivariate time series dataset collected exclusively from a single anonymized engine component (Component X) across a fleet of SCANIA trucks. The dataset includes operational data, repair records, and specifications related to Component X while maintaining confidentiality through anonymization. It is well-suited for a range of machine learning applications, including classification, regression, survival analysis, and anomaly detection, particularly in predictive maintenance scenarios. The dataset’s large population size, diverse features (in the form of histograms and numerical counters), and temporal information make it a unique resource in the field. The objective of releasing this dataset is to give a broad range of researchers the possibility of working with real-world data from an internationally well-known company and introduce a standard benchmark to the predictive maintenance field, fostering reproducible research.

In the dynamic landscape of industrial operations, ensuring machines operate without interruption is crucial for maintaining optimal productivity levels. Estimating the Remaining Useful Life within Predictive Maintenance is vital for minimizing downtime, improving operational efficiency, and prevent-ing unexpected equipment failures. Survival analysis is a beneficial approach in this context due to its power of handling censored data (here referred to industrial assets that have not experienced a failure during the study period). However, the black-box nature of survival analysis models necessitates the use of explainable AI for greater transparency and interpretability. This study evaluates three Machine Learning-based Survival Analysis models and a traditional Survival Analysis model using real-world data from SCANIA AB, which includes over 90% censored data. Results indicate that Random Survival Forest outperforms the Cox Proportional Hazards model and the Gradient Boosting Survival Analysis and Survival Support vector machine. Additionally, we employ SHAP analysis to provide global and local explanations, highlighting the importance and interaction of features in our best-performing model. To overcome the limitation of applying SHAP on survival output, we utilize a surrogate model. Finally, SHAP identifies specific influential features, shedding light on their effects and interactions. This compre-hensive methodology tackles the inherent opacity of machine learning-based survival analysis models, providing valuable insights into their predictive mechanisms. The findings from our SHAP analysis underscore the pivotal role of these identified features and their interactions, thereby enriching our comprehension of the factors influencing Remaining Useful Life predictions.

This paper presents SurvLoss, a novel asymmetric partial loss and error calculation function for survival analysis and regression, enabling the inclusion of censored samples. An observation in a dataset for which the complete information regarding an event of interest is not available is called censored. Censored samples are ubiquitous in the industry and play a crucial role in Prognostics and Health Management (PHM) by providing a realistic representation of data, improving the accuracy of analyses, and supporting better decision-making in various industries and the healthcare sector. The proposed approach can effectively equip the conventional regression loss functions such as Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE) with the ability to process censored samples. This can impact the field hugely by providing a more accessible usage of neural network models in survival analysis. The proposed survival loss incorporates censored samples by penalizing predictions outside the censoring region and skipping them otherwise. Then, it uses weighted averaging to aggregate the loss from censored samples with the loss from event samples.

Unlike many other methods in the field, the proposed model distinguishes itself by avoiding superficial assumptions and exclusively relies on the available information, considering the entirety of the data.

We compared the proposed loss function with its baseline on two publicly available datasets. The first dataset, called C-MAPSS, is from NASA Turbofan Jet Engines simulation, and the second is a recently published real-world dataset from

SCANIA trucks. The goal of both datasets is to predict the remaining useful life (RUL) of the machines. The experimental results show that optimization algorithms for training deep neural networks like Adam can effectively utilize the proposed loss function to calculate gradients, update the model’s weights, and reduce training and test errors. Moreover, the

proposed model outperformed the baseline by taking advantage of the censored samples. The proposed loss function paves the way for the employment of advanced architectures of neural networks with bigger training sizes in survival analysis.

This study is a collaboration between data scientists, innovation management researchers from academia, and experts from a hygiene and health company. The study aims to develop an automatic idea detection package to control and prevent healthcare-associated infections (HAI) by extracting informative ideas from social media using Active Learning and Transfer Learning. The proposed package includes a dataset collected from Twitter, expert-created labels, and an annotation framework. Transfer Learning has been used to build a twostep deep neural network model that gradually extracts the semantic representation of the text data using the BERTweet language model in the first step. In the second step, the model classifies the extracted representations as informative or non-informative using a multi-layer perception (MLP). The package is named AID4HAI (Automatic Idea Detection for controlling and preventing Healthcare-Associated Infections) and is publicly available on GitHub.

This research is an interdisciplinary work between data scientists, innovation management researchers and experts from Swedish academia and a hygiene and health company. Based on this collaboration, we have developed a novel package for automatic idea detection with the motivation of controlling and preventing healthcare-associated infections (HAI). The principal idea of this study is to use machine learning methods to extract informative ideas from social media to assist healthcare professionals in reducing the rate of HAI. Therefore, the proposed package offers a corpus of data collected from Twitter, associated expert-created labels, and software implementation of an annotation framework based on the Active Learning paradigm. We employed Transfer Learning and built a two-step deep neural network model that incrementally extracts the semantic representation of the collected text data using the BERTweet language model in the first step and classifies these representations as informative or non-informative using a multi-layer perception (MLP) in the second step. The package is called AID4HAI (Automatic Idea Detection for controlling and preventing Healthcare-Associated Infections) and is made fully available (software code and the collected data) through a public GitHub repository. We believe that sharing our ideas and releasing these ready-to-use tools contributes to the development of the field and inspires future research.