Search Results (5)

The use of electric drives and energy storage devices in vehicles presents fresh challenges for system designers. Among these is addressing the susceptibility of battery packs to mechanical vibrations, necessitating vibration testing. In failure scenarios, like a battery fire, swiftly detaching the battery pack from the vibration platform is vital. It is also essential to ensure that the mounting system—fixture and fastener—effectively transfers vibration between the exciter and the battery pack. The article discusses the basic requirements for the fixture of specimens subjected to vibration testing and fastening it to a slip table of head expander, giving a better understanding of its role. It then presents the results of a theoretical analysis of the fixing forces and their laboratory testing using prototype customized fastening solutions with potential for use in vibration testing. The results of the conducted research and analyses demonstrate that non-standard mounting techniques have limited potential to replace screw mountings in vibration testing, particularly as fully universal techniques. However, the generated mounting forces, with potential resulting from the possibility of tailored implementation of the tested mounting techniques in the design of tables or head expanders, appropriately designed, justify further research work in this area. Full article

The rail fastening system plays a crucial role in railway tracks as it ensures operational safety by fixing the rail on to the sleeper. Early detection of rail fastener system defects is crucial to ensure track safety and to enable maintenance optimization. Fastener inspections are normally conducted either manually by trained maintenance personnel or by using automated 2-D visual inspection methods. Such methods have drawbacks when visibility is limited, and they are also found to be expensive in terms of system maintenance cost and track possession time. In a previous study, the authors proposed a train-based differential eddy current sensor system based on the principle of electromagnetic induction for fastener inspection that could overcome the challenges mentioned above. The detection in the previous study was carried out with the aid of a supervised machine learning algorithm. This study reports the finding of a case study, along a heavy haul line in the north of Sweden, using the same eddy current sensor system mounted on an in-service freight train. In this study, unsupervised machine learning models for detecting and analyzing missing clamps in a fastener system were developed. The differential eddy current measurement system was set to use a driving field frequency of 27 kHz. An anomaly detection model combining isolation forest (IF) and connectivity-based outlier factor (COF) was implemented to detect anomalies from fastener inspection measurements. To group the anomalies into meaningful clusters and to detect missing clamps within the fastening system, an unsupervised clustering based on the DBSCAN algorithm was also implemented. The models were verified by measuring a section of the track for which the track conditions were known. The proposed anomaly detection model had a detection accuracy of 96.79% and also exhibited a high score of sensitivity and specificity. The DBSCAN model was successful in clustering missing clamps, both one and two missing clamps, from a fastening system separately. Full article

In the current study, the coupling of a cooling problem with the electromagnetic resonance of a bulk acoustic wave (BAW) material is investigated. As well, a new cooling method by the addition of nanoparticles to a phase change material surrounding the BAW resonator is presented. To solve the governing equations of piezoelectric charge and momentum balance, thermal balance, and fluid flow a code with the method of finite element is introduced. After validation of various features of the code with melting profile, heat generation, charge curve, and dispersion curve with benchmarks, the eigenfrequency analysis of the system is done. The thermal behavior of the system at first mode and various boundary conditions are studied. As well, the effect of nanoparticles in fastening the cooling of the BAW resonator is demonstrated. Full article

The rail fastening system forms an integral part of rail tracks, as it maintains the rail in a fixed position, upholding the track stability and track gauge. Hence, it becomes necessary to monitor their conditions periodically to ensure safe and reliable operation of the railway. Inspection is normally carried out manually by trained operators or by employing 2-D visual inspection methods. However, these methods have drawbacks when visibility is minimal and are found to be expensive and time consuming. In the previous study, the authors proposed a train-based differential eddy current sensor system that uses the principle of electromagnetic induction for inspecting the railway fastening system that can overcome the above-mentioned challenges. The sensor system includes two individual differential eddy current sensors with a driving field frequency of 18 kHz and 27 kHz respectively. This study analyses the performance of a machine learning algorithm for detecting and analysing missing clamps within the fastening system, measured using a train-based differential eddy current sensor. The data required for the study was collected from field measurements carried out along a heavy haul railway line in the north of Sweden, using the train-based differential eddy current sensor system. Six classification algorithms are tested in this study and the best performing model achieved a precision and recall of 96.64% and 95.52% respectively. The results from the study shows that the performance of the machine learning algorithms improved when features from both the driving channels were used simultaneously to represent the fasteners. The best performing algorithm also maintained a good balance between the precision and recall scores during the test stage. Full article

In most industrial fields, mechanical clamping methods are traditionally used to transport heavy loads such as steel structures by fastening the load using bolts and nuts. However, this method can lead to industrial accidents during load transfer and does not consider the weight of the load. Recently, permanent magnet clamping methods have been proposed to prevent such accidents; for example, hybrid electromagnetic clamping systems (H-EMCSs), which combine permanent magnets and electromagnets and can adjust the clamping force according to the load weight. However, few studies have attempted to improve the electromagnetic structure and effective magnetic flux of H-EMCS. Specifically, H-EMCSs control the clamping force using several hybrid electromagnetic modules (H-EMMs); however, the leakage magnetic flux increases with an increasing number of H-EMMs. Therefore, the clamping force should be improved to avoid increasing the leakage magnetic flux. In this study, we propose a novel H-EMM structure and improve its electromagnetic force characteristics by changing the core shape and dimension effect in order to reduce the leakage flux and maximize the effective magnetic flux. Furthermore, we verify the improved electromagnetic force properties by experimentally validating the proposed model. This research can improve the safe and effective transfer of industrial loads. Full article