Machinery Fault Detection Through Ultrasound Technology

Ultrasound is a versatile advanced technology that is utilized in the oil and gas industry for various mechanical and electrical applications such as bearing's faults detection, pump's cavitation, valve's leakage, steam traps, electrical faults, gearbox's issues, compressed air and gas leak's detection..etc. The technology allows the end-user to measure dynamic data using contact (Structure borne) and non-contact (air borne) sensors and converts the ultrasound waves to an audible range for humans to associate sounds with the measured signal. As a result, the sound of the machine can be heard and recorded as voice clip as well as time wave form, which in turn can be translated into frequency spectrum for analysis. The technology has recently evolved in the industry as an important condition monitoring tool, to increase the reliability of rotating equipment. Moreover, it used as a complementary tool to vibration analysis. As well, it can be used as a tool for troubleshooting and preventive maintenance inspection. Background Ultrasound is sound waves with frequencies that are higher than the upper audible limit of human hearing. The human hearing limit varies from person to another, and it is approximated to be around 20Hz to 20 kHz. This is in contrary to the ultrasound range, which is above 20,000 Hz, and hence, it is in audible to human. This range is used widely in various industrial processes, including: cleaning, cutting, forming, testing of materials, and welding. It is characterized by its directional waves, unlike normal sound waves that travel in all directions. This directional characteristic makes ultrasound useful for many applications. Furthermore, ultrasound technology is used in different fields: medical, automotive, etc. and recently in the oil and gas industry as non-destructive-testing tool (NDT). The ultrasound technology in the oil and gas industry is used primarily in the following area's, for example Leak detection. Steam traps inspection. Bearing condition monitoring. Bearing lubrication monitoring. Electrical Inspection. Valve condition monitoring. Pump cavitation. Gearbox issues.

Brinell Fault Injection to Enable Development of a Low-Cost Wheel Bearing Fault Monitoring System for Automobiles

Although bearing condition monitoring and fault diagnosis is a widely studied and mature field, applications to automotive wheel bearings have received little attention. This is likely due to the lack of business case, as the vehicle’s four wheel bearings are typically designed to last the vehicle life with low failure rates. Rapid advancements in battery technology are expected to open the door for EVs with million-mile lifespans, exceeding the reliable life of existing low-cost wheel bearing designs. Vehicle designers and fleet owners must choose between paying a higher price for bearings with a longer life or replacing wheel bearings periodically throughout the vehicle life. The latter strategy can be implemented most effectively with the implementation of a low-cost fault detection system on the vehicle. To develop such a system, data from systems with healthy and faulty wheel bearings is needed. This paper discusses the options for generating this data, such as simulation, bench tests, and vehicle-level tests. The challenges and limitations of each are explored, and the specific challenges of developing an approach for a wheel bearing fault detection system are discussed in detail. A method for injecting Brinell Dent failures is developed, and the results of injecting a total of 40 faulty wheel bearings are presented. Metrics of measuring and summarizing the ground-truth health of a wheel bearing using vibration signals recorded on a test bench are explored. These wheel bearings are used to collect preliminary vehicle data, and some initial analysis is shared highlighting the differences between healthy and faulty wheel bearings, setting the stage for future work to develop a low-cost wheel bearing fault detection system.

Fault Dynamic Modeling and Characteristic Parameter Simulation of Rolling Bearing with Inner Ring Local Defects

To further study the fault mechanism and fault features of rolling bearings, a two-DOF rolling bearing fault dynamic model with inner ring local defects considering the bearing radial clearance and time-varying displacement excitation is established based on Hertz contact theory. By comparing the simulated fault signal with bearing fault test data in the time domain and frequency domain, the accuracy of the established fault dynamic model is verified. Finally, the change rules of the characteristic parameters of the bearing inner ring fault signal, including effective value, absolute mean value, square root amplitude, peak value, kurtosis factor, pulse factor, peak factor, and shape factor, are simulated by the fault dynamic model. The results highlight that the proposed fault dynamic model is in good agreement with the experimental results. The model can simulate the fault signal characteristic parameters with the change of defect width, external load, and rotating speed effectively. The study in this paper is of engineering application value for bearing condition monitoring and fault diagnosis.

Property requirements of vibration measurements in wind turbine drivetrain bearing condition monitoring

Wind turbine drivetrain bearing failures continue to lead to high costs resulting from turbine downtime and maintenance. As the standardised tool to best avoid downtime is online vibration condition monitoring, a lot of research into improving the signal analysis tools of the vibration measurements is currently being performed. However, failures in the main bearing and planetary gears are still going undetected in large numbers. The available field data is limited when it comes to the properties of the stored measurements. Generally, the measurement time and the covered frequency range of the stored measurements are limited compared to the data used in real-time monitoring. Therefore, it is not possible to either reproduce the monitoring or to evaluate new tools developed through research for signal analysis and diagnosis using the readily available field data. This study utilises 12 bearing failures from wind turbine condition monitoring systems to evaluate and make recommendations concerning the optimal properties in terms of measurement time and frequency range the stored measurements should have. The results show that the regularly stored vibration measurements that are available today are, throughout most of the drivetrain, not optimal for research-driven postfailure investigations. Therefore, the storage of longer measurements covering a wider frequency range needs to begin, while researchers need to demand this kind of data.

Bearing fault detection under time-varying speed based on empirical wavelet transform, cultural clan-based optimization algorithm, and random forest classifier

Bearings are massively utilized in industries of nowadays due to their huge importance. Nevertheless, their defects can heavily affect the machines performance. Therefore, many researchers are working on bearing fault detection and classification; however, most of the works are carried out under constant speed conditions, while bearings usually operate under varying speed conditions making the task more challenging. In this paper, we propose a new method for bearing condition monitoring under time-varying speed that is able to detect the fault efficiently from the vibration signatures. First, the vibration signal is processed with the Empirical Wavelet Transform to extract the AM-FM modes. Next, time domain features are calculated from each mode. Then, the features’ set is reduced using the Cultural Clan-based optimization algorithm by removing the redundant and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm “Random Forest” is used to train a model able to classify the fault based on the selected features. The proposed method was tested on a time-varying real dataset consisting of three different bearing health states: healthy, outer race defect, and inner race defect. The obtained results indicate the ability of our proposed method to handle the speed variability issue in bearing fault detection with high efficiency.

Degradation assessment of bearing based on machine learning classification matrix

In the broad framework of degradation assessment of bearing, the final objectives of bearing condition monitoring is to evaluate different degradation states and to estimate the quantitative analysis of degree of performance degradation. Machine learning classification matrices have been used to train models based on health data and real time feedback. Diagnostic and prognostic models based on data driven perspective have been used in the prior research work to improve the bearing degradation assessment. Industry 4.0 has required the research in advanced diagnostic and prognostic algorithm to enhance the accuracy of models. A classification model which is based on machine learning classification matrix to assess the degradation of bearing is proposed to improve the accuracy of classification model. Review work demonstrates the comparisons among the available state-of-the-art methods. In the end, unexplored research technical challenges and niches of opportunity for future researchers are discussed.

Defect detection in freight railcar tapered-roller bearings using vibration techniques

Currently, there are two types of defect detection systems used to monitor the health of freight railcar bearings in service: wayside hot-box detection systems and trackside acoustic detection systems. These systems have proven to be inefficient in accurately determining bearing health, especially in the early stages of defect development. To that end, a prototype onboard bearing condition monitoring system has been developed and validated through extensive laboratory testing and a designated field test in 2015 at the Transportation Technology Center, Inc. in Pueblo, CO. The devised system can accurately and reliably characterize the health of bearings based on developed vibration thresholds and can identify defective tapered-roller bearing components with defect areas smaller than 12.9 cm2 while in service.