Active Lubricant Condition Monitoring

Lubrication in a mechanical system is like blood flowing through a human body. It is a great source to detect in advance any troubles in the system. As blood analysis shows the diseases in our body in a machine the oil analysis shows the fault in a system. Active lubricant condition monitoring is a great prediction technique which would help improve the reliability and reduce the maintenance costs for a system. The paper discusses about how to develop an Active oil condition monitoring where oil condition can be monitored online and how to achieve an active maintenance action. It discusses the design and setup of an oil condition monitoring system on a test bench provided. Different oil degradation parameters are analysed and sensors are introduced to monitor this parameters. Integration of microcontroller for wireless communication and use of different software’s to acquire and process the data from the sensors to provide the real time condition of the system is discussed. This includes different actuation system that can be introduced to help the maintenance of the system to minimum and reduce the maintenance or damaging of the system components

A New Method for Monitoring Gears Surface Failures Using Enhanced Image Registration Approach

In this paper, we present an image registration approach to cope with inter-image illumination changes of arbitrary shape in order to monitor the development of micro-pitting in transmission gears. Traditional image registration approaches do not typically account for inter-image illumination variations that negatively affect the geometric registration precision. Given a set of captured images of gear surface degradation with different exposure times and geometric deformations, the correlation between the resulting aligned images is compared to a reference one. The presented image registration approach is used with an online health monitoring system involving the analysis of vibration, acoustic emission and oil debris to follow the development of micro-pitting in transmission gears. The proposed monitoring system achieves more registration precision compared to competing systems. This paper experimentally validates the system's capabilities to detect early gear defects and reliably identify the gradual development of micro-pitting in gears, so that it could be used in predictive health monitoring (PHM) systems and overcome the disadvantages of the most commonly used methods, such as gear flank profile scanning, replica sample analysis and conventional image analysis

Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles

One of the primary tasks undertaken by autonomous vehicles (AVs) is object detection, which comes ahead of object tracking, trajectory estimation, and collision avoidance. Vulnerable road objects (e.g., pedestrians, cyclists, etc.) pose a greater challenge to the reliability of object detection operations due to their continuously changing behavior. The majority of commercially available AVs, and research into them, depends on employing expensive sensors. However, this hinders the development of further research on the operations of AVs. In this paper, therefore, we focus on the use of a lower-cost single-beam LiDAR in addition to a monocular camera to achieve multiple 3D vulnerable object detection in real driving scenarios, all the while maintaining real-time performance. This research also addresses the problems faced during object detection, such as the complex interaction between objects where occlusion and truncation occur, and the dynamic changes in the perspective and scale of bounding boxes. The video-processing module works upon a deep-learning detector (YOLOv3), while the LiDAR measurements are pre-processed and grouped into clusters. The output of the proposed system is objects classification and localization by having bounding boxes accompanied by a third depth dimension acquired by the LiDAR. Real-time tests show that the system can efficiently detect the 3D location of vulnerable objects in real-time scenarios

Monitoring of upper-limb EMG signal activities using a low cost system: Towards a power-assist robotic arm

Many human activities depend on upper-limb motion, which can be characterized and estimated using the activation levels of the electromyography (EMG) signal of the upper-limb muscles. Researchers are devoting much effort to investigating these activities during elbow extension and flexion. Also, a few studies have concluded with the development of a power-assisted arm. However, the systems introduced so far are expensive and there are long waiting lists of people requesting such systems. The aim of the present work is to develop a power-assist arm based on the EMG signal activities of the upper-limb, and this paper describes the first part of this study focusing on the monitoring of EMG signals during upper limb activities based on the development of a low-cost system. The relationship between elbow motion and the activity level of the biceps muscle is characterised and different relevant features are logged. The new low-cost system is then validated against the Biopack specialised biomedical measurement system

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Development of an Advanced Diagnostic System for Automotive Mechanical Transmissions

Automotive transmission is one of the most important parts of any vehicle powertrain system, and in order to achieve reliable operation, effective health monitoring must be used. Predictive health monitoring (PHM) systems are currently gaining in popularity due to their effectiveness in reducing maintenance costs; however, reliable monitoring techniques are required such as the analysis of vibration, acoustic emissions and oil debris. In this paper, different monitoring techniques and their features are studied in order to develop an advanced monitoring system able to track the condition of an operating transmission system, classify faults, and detect the onset of failure. The study presents an online PHM system utilising autoregressive (AR) parametric algorithms, time and frequency analysis based on wireless transmission of vibration data. The online monitoring algorithm can support CBM and PHM of automotive multistage manual transmissions. The design, operation and validation of the online system are described and demonstrated. The results of the experimental test prove the system's capability and support the recent trend of using CBM and PHM strategies

Clinical and Radiographic Predictors of Successful Coronary Angiography Through Right Radial Artery Access

Background: One of the limitations of the right radial access approach is complex vessel anatomy, such as subclavian tortuosity. Several clinical predictors have been proposed for tortuosities, such as older age, female sex and hypertension. In this study, we hypothesised that chest radiography would add predictive value to the traditional predictors. Methods: This prospective blinded study included patients who underwent transradial access coronary angiography. They were classified into four groups according to difficulty: Group I, Group II, Group III and Group IV. Different groups were compared according to clinical and radiographic characteristics. Results: The study included 108 patients (54, 27, 17 and 10 patients in Groups I, II, III and IV, respectively). The rate of crossover to transfemoral access was 9.26%. Age, hypertension and female sex were associated with a greater difficulty and failure rates. Regarding radiographic parameters, a higher failure rate was associated with a higher diameter of the aortic knuckle (Group IV, 4.09 ± 1.32 cm versus Groups I, II and III combined, 3.26 ± 0.98 cm; p=0.015) and the width of the mediastinum (Group IV, 8.96 ± 2.88 cm versus Groups I, II and III combined, 7.28 ± 1.78 cm; p=0.009). The cut-off value for prominent aortic knuckle was 3.55 cm (sensitivity 70% and specificity 67.35%) and the width of mediastinum was 6.59 cm (sensitivity 90% and specificity 42.86%). Conclusion: Radiographic prominent aortic knuckle and wide mediastinum are valuable clinical parameters and useful predictors for transradial access failure caused by tortuosity of the right subclavian/brachiocephalic arteries or aorta

A Lightweight Network for Real-Time Rain Streaks and Rain Accumulation Removal from Single Images Captured by AVs

In autonomous driving, object detection is considered a base step to many subsequent processes. However, object detection is challenged by loss in visibility caused by rain. Rainfall occurs in two main forms, which are streaks and streaks accumulations. Each degradation type imposes different effect on the captured videos; therefore, they cannot be mitigated in the same way. We propose a lightweight network which mitigates both types of rain degradation in real-time, without negatively affecting the object-detection task. The proposed network consists of two different modules which are used progressively. The first one is a progressive ResNet for rain streaks removal, while the second one is a transmission-guided lightweight network for rain streak accumulation removal. The network has been tested on synthetic and real rainy datasets and has been compared with state-of-the-art (SOTA) networks. Additionally, time performance evaluation has been performed to ensure real-time performance. Finally, the effect of the developed deraining network has been tested on YOLO object-detection network. The proposed network exceeded SOTA by 1.12 dB in PSNR on the average result of multiple synthetic datasets with 2.29× speedup. Finally, it can be observed that the inclusion of different lightweight stages works favorably for real-time applications and could be updated to mitigate different degradation factors such as snow and sun blare