886 research outputs found
A comparative study of misalignment detection using a novel Wireless Sensor with conventional Wired Sensors
No full textThe advancement in low cost and low power MEMS sensors makes it possible to develop a cost-effective wireless accelerometer for condition monitoring. Especially, the MEMS accelerometer can be mounted directly on a rotating shaft, which has the potential to capture the dynamics of the shaft more accurately and hence to achieve high monitoring performance. In this paper a systematic comparison of shaft misalignment detection is conducted, based on a bearing test rig, between the wireless sensor measurement scheme and other three common sensors: a laser vibrometer, an accelerometer and a shaft encoder. These four sensors are used to measure simultaneously the dynamic responses: Instantaneous Angular Speed (IAS) from the encoder, bearing house acceleration from the accelerometer, shaft displacements from the laser vibrometer and angular acceleration from the wireless sensor. These responses are then compared in both the time and frequency domains in detecting and diagnosing different levels of shaft misalignment. Results show the effectiveness of wireless accelerometer in detecting the faults
Identification of lubrication Regimes in Mechanical Seals using Acoustic Emission for Condition Monitoring
No full textThe quality of lubrication condition between seal faces directly affects the reliability, operating life and sealing performance of mechanical seals. Thus, the identification of lubrication regimes in face seals i.e. boundary lubrication (BL), mixed lubrication (ML) and hydrodynamic lubrication (HL) is of high importance for developing effective online condition monitoring approaches. This paper investigates the tribological behaviour and frictional characteristics of mechanical seals based on nonintrusive acoustic emission (AE) measurements. Mathematical models for AE generation mechanisms are derived based on the tribological behaviour and operating parameters of mechanical seals. They produce agreeable results with experimental data in explaining the types of AE signals observed in monitoring the face lubrication conditions. Frequency domain analysis of data shows that the viscous friction process generates more low frequency AE signals, whereas the asperity interactions show more high frequency AE. Moreover, the feasibility of using statistical parameters of the time domain data is shown to identify the lubrication regimes in face seals
Dynamic simulation of a roller rig
No full textThe dynamic behaviour of railway vehicles is greatly influenced by the interaction of the wheelsets on the railway track. This behaviour can be replicated in laboratory conditions using a scaled roller rig. This paper presents the results of the modeling and simulation of a one-fifth scale roller rig. The simulation results obtained have been compared with a real railway vehicle (bogie) on the track. It has been observed that the scale roller rig has a much lower critical velocity than real railway vehicle due to the similarity and dimensional scaling laws that are taken into consideration in the roller rig design. The scale roller rig critical speed was observed to be 10.2 m/s while the Full Scale railway vehicle model was found to be 52 m/s. It can be concluded that the critical speed of the scaled roller rig model is one-fifth of the full scale critical speed
Characterization of Acoustic Emissions from Mechanical Seals for Fault Detection
No full textThe application of high-frequency Acoustic Emissions (AE) for mechanical seals diagnosis is gaining acceptance as a useful complimentary tool. This paper investigates the AE characteristics of mechanical seals under different rotational speed and fluid pressure (load) for develop a more comprehensive monitoring method. A theoretical relationship between friction in asperity contact and energy of AE signals is developed in present work. This model demonstrates a clear correlation between AE Root Mean Square (RMS) value and sliding speed, contact load and number of contact asperities. To benchmark the proposed model, a mechanical seal test rig was employed for collecting AE signals under different operating conditions. Then, the collected data was processed using time domain and frequency domain analysis methods to suppressing noise interferences from mechanical system for extracting reliably the AE signals from mechanical seals. The results reveal the potential of AE technology and data analysis method applied in this work for monitoring the contact condition of mechanical seals, which will be vital for developing a comprehensive monitoring systems and supporting the optimal design and operation of mechanical seals
Prediction of metal pm emission in rail tracks for condition monitoring application
No full textExposure to particulate material (PM) is a major health concern in megacities across the world which use trains as a primary public transport. PM emissions caused by railway traffic have hardly been investigated in the past, due to their obviously minor influence on the atmospheric air quality compared to automotive transport. However, the electrical train releases particles mainly originate from wear of rails track, brakes, wheels and carbon contact stripe which are the main causes of cardio-pulmonary and lung cancer. In previous reports most of the researchers have focused on case studies based PM emission investigation. However, the PM emission measured in this way doesn’t show separately the metal PM emission to the environment. In this study a generic PM emission model is developed using rail wheel-track wear model to quantify and characterise the metal emissions. The modelling has based on Archard’s wear model. The prediction models estimated the passenger train of one set emits 6.6mg/km-train at 60m/s speed. The effects of train speed on the PM emission has been also investigated and resulted in when the train speed increase the metal PM emission decrease. Using the model the metal PM emission has been studied for the train line between Leeds and Manchester to show potential emissions produced each day. This PM emission characteristics can be used to monitor the brakes, the wheels and the rail tracks conditions in future
Application of Phase Space Warping on Damage Tracking for Bearing Fault
No full textNowadays, the significance of keeping equipment function properly each time is obvious. If equipment fails during its use, it may have disastrous consequences. Estimating remaining useful life (RUL) of equipment is a key to prevent such calamities, improve its reliability, provide security and reduce unnecessary maintenance and operational cost. The evolution and tracking of damage is the foundation of RUL predicting, and also is one of the most important content of mechanical fault diagnosis. Slow-time variable process of mechanical damage would lead the phase space reconstructed by fast-time variable vibrate signals warping. Search the dynamics characteristic law of damage evolution analysis in the phase space, and build the relationship between fast-time variable signals and slow-time variable damage, and then damage evolution tracking is possible. To validate the theory, simulation model of bearing damage evolution is built, the outer-race fault evolution signals is obtained, and the trend of evolution of degradation of bearing fault is described with Phase Space Warping (PSW) theory and Smooth Orthogonal Decomposition (SOD). The results proved the feasibility of the methodology of PSW in damage evolution tracking
Prediction of metal pm emission in rail tracks for condition monitoring application
No full textExposure to particulate material (PM) is a major health concern in megacities across the world which use trains as a primary public transport. PM emissions caused by railway traffic have hardly been investigated in the past, due to their obviously minor influence on the atmospheric air quality compared to automotive transport. However, the electrical train releases particles mainly originate from wear of rails track, brakes, wheels and carbon contact stripe which are the main causes of cardio-pulmonary and lung cancer. In previous reports most of the researchers have focused on case studies based PM emission investigation. However, the PM emission measured in this way doesn’t show separately the metal PM emission to the environment. In this study a generic PM emission model is developed using rail wheel-track wear model to quantify and characterise the metal emissions. The modelling has based on Archard’s wear model. The prediction models estimated the passenger train of one set emits 6.6mg/km-train at 60m/s speed. The effects of train speed on the PM emission has been also investigated and resulted in when the train speed increase the metal PM emission decrease. Using the model the metal PM emission has been studied for the train line between Leeds and Manchester to show potential emissions produced each day. This PM emission characteristics can be used to monitor the brakes, the wheels and the rail tracks conditions in future
Motor Current Signature Analysis of a Variable Speed Drive for Motor Fault Diagnosis
No full textThe induction motor is one of the most used electric machines in the industry because of its strong and simplicity. This paper investigates the performance of conventional techniques such as sideband analysis in detecting broken rotor bars when the motor is fed from a common pulse width modulation voltage source inverter PWM-VSI drive for variable speed drives. The phase current signals are obtained under both the slip compensation mode and non-compensation mode. The spectra are compared between the healthy and faulty motors in the frequency domain. For the non-slip compensation, it has shown clear sidebands of the twice slip frequency which allows the detection of the broken bar and hence rotor faults under different speeds and higher loads. However, for slip compensation, the sideband pattern does not exist anymore and hence new features have to be investigated for fault detection when motors operate under these operating modes
Modelling acoustic emissions generated by tribological behaviour of mechanical seals for condition monitoring and fault detection
Get PDFAcoustic emission (AE) signals are useful for the condition monitoring of mechanical seals as tribological regimes affect the AE signatures. In this paper the investigation develops a mathematical model that can predict the energy of an AE signal under different tribological regimes. The developed model has been validated with experimental studies and satisfactory results have been perceived. Therefore, the model has strong potential to be used to obtain tribological behaviour of mechanical seals and hence develop a reliable and accurate condition monitoring system under varying operating conditions.</p
A Lightweight Parallel Convolutional Model for Abnormal Detection and Classification of Universal Robots Under Varied Load Conditions
No full textWith the advancement of modern industrial automation and smart manufacturing, the demand for robots to perform precise operations has increased dramatically. Robots, with their highly repetitive movements and operations in diverse and complex environments, are prone to faults, posing challenges to production efficiency and equipment reliability. In order to avoid the cost of incorporating additional sensors, this study directly uses the feedback data generated by the intrinsic control system of universal robots for condition monitoring. An innovative lightweight parallel convolutional model is developed to facilitate the extraction and learning of multi-layered features, which leverages position and force data as inputs. The design of the dual-stream residual structure allows the model to capture feature information with lower parameter complexity, enhancing data processing efficiency. The multi-scale feature enhancement module improves the adaptability and robustness of the model under different working conditions, providing technical support for rapid diagnostics in practice. Experimental datasets demonstrate the model's capability in abnormal detection and classification under various load conditions.</p
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