Railway track surface faults dataset.

Railway infrastructure maintenance is critical for ensuring safe and efficient transportation networks. Railway track surface defects such as cracks, flakings, joints, spallings, shellings, squats, grooves pose substantial challenges to the integrity and longevity of the tracks. To address these challenges and facilitate further research, a novel dataset of railway track surface faults has been presented in this paper. It is collected using the EKENH9R cameras mounted on a railway inspection vehicle. This dataset represents a valuable resource for the railway maintenance and computer vision related scientific communities. This dataset includes a diverse range of real-world track surface faults under various environmental conditions and lighting scenarios. This makes it an important asset for the development and evaluation of Machine Learning (ML), Deep Learning (DL), and image processing algorithms. This paper also provides detailed annotations and metadata for each image class, enabling precise fault classification and severity assessment of the defects. Furthermore, this paper discusses the data collection process, highlights the significance of railway track maintenance, emphasizes the potential applications of this dataset in fault identification and predictive maintenance, and development of automated inspection systems. We encourage the research community to utilize this dataset for advancing the state-of-the-art research related to railway track surface condition monitoring.

Triaxial bearing vibration dataset of induction motor under varying load conditions.

Rotating machines as core component of an industry can effectively be monitored through vibration analysis. Considering the importance of vibration in industrial condition monitoring, this article reports and discusses triaxial vibration data for motor bearing faults detection and identification. The data is acquired using a MEMS based triaxial accelerometer and the National Instruments myRIO board. The bearing conditions include healthy bearing, bearings with inner race faults, and bearings with outer race faults. For each faulty bearing condition, the three-phase induction motor is operated under three different load conditions. The dataset can be used to assess performance of newly developed methods for effective bearing fault diagnosis. Mendeley Data. http://dx.doi.org/10.17632/fm6xzxnf36.2.