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

Proceedings of COMADEM 201

Robust damage detection in smart structures

This thesis is devoted to present some novel techniques in Structural Health Monitoring (SHM). SHM is a developing field that tries to monitor structures to make sure that they remain in their desired condition to avoid any catastrophe. SHM includes different levels from damage detection area to prognosis field. This work is dedicated to the first level, which might be considered the main and most important level. New techniques presented in this work are based on different statistical and signal processing methods such as Principal Component Analysis and its robust counterpart, Wavelet Transform, Fuzzy similarity, Andrew plots, etc. These techniques are applied on the propagated waves that are activated and captured in the structure using appropriate transducers. Piezoceramic (PZT) devices are chosen in this work to capture the signals due to their special characteristics such as high performance, low energy consumption and reasonable price. To guarantee the efficiency of the suggested techniques, they are tested on different laboratory and real scale test benchmarks, such as aluminum and composite plates, fuselage, wing skeleton, tube, etc. Because of the variety of tested benchmarks, this thesis is called damage detection in smart structures. This variety may promise the ability and capability of the proposed methods on different fields such as aerospace and gas/oil industry. In addition to the normal laboratory conditions, it is shown in this work that environmental changes can affect the performance of the damage detection and wave propagation significantly. As such, there is a vital need to consider their effect. In this work, temperature change is chosen as it is one of the main environmental fluctuation factors. To scrutinize its effect on damage detection, first, the effect of temperature is considered on wave propagation and then all the proposed methods are tested to check whether they are sensitive to temperature change or not. Finally, a temperature compensation method is applied to ensure that the proposed methods are stable and robust even when structures are subjected to variant environmental conditions.La presente tesis doctoral se dedica a la exploración y presentación de técnicas novedosas para la Monitorización y detección de defectos en estructuras (Structural Health Monitoring -SHM-) SHM es un campo actualmente en desarrollo que pretende asegurarse que las estructuras permanecen en su condición deseada para evitar cualquier catástrofe. En SHM se presentan diferentes niveles de diagnóstico, Este trabajo se concentra en el primer nivel, que se considera el más importante, la detección de los defectos. Las nuevas técnicas presentadas en esta tesis se basan en diferentes métodos estadísticos y de procesamiento de señales tales como el Análisis de Componentes Princpales (PCA) y sus variaciones robustas, Transformada wavelets, lógica difusa, gráficas de Andrew, etc. Estas técnicas de aplican sobre las ondas de vibración que se generan y se miden en la estructura utilizando trasductores apropiados. Dispositivos piezocerámicos (PZT's) se han escogido para este trabajo ya que presentan características especiales tales como: alto rendimiento, bajo consumo de energia y bajo costo. Para garantizar la eficacia de la metodología propuesta,se ha validado en diferentes laboratorios y estructuras a escala real: placas de aluminio y de material compuesto, fuselage de un avión, revestimiento del ala de un avóin, tubería, etc. Debido a la gran variedad de estructuras utilizadas, su aplicación en la industria aeroespacial y/o petrolera es prometedora. Por otra parte, los cambios ambientales pueden afectar al rendimiento de la detección de daños y propagación de la onda significativamente . En este trabajo , se estudia el efecto de las variaciones de temperatura ya que es uno de los principales factores de fluctuación del medio ambiente . Para examinar su efecto en la detección de daños, en primer lugar, todos los métodos propuestos se prueban para comprobar si son sensibles a los cambios de temperatura o no. Finalmente , se aplica un método de compensación de temperatura para garantizar que los métodos propuestos son estables y robustos incluso cuando las estructuras se someten a condiciones ambientales variante

Quantifying the damage of in-service rolling stock wheelsets using remote condition monitoring

The global railway network is set to continue to expand in terms of size, passenger numbers and freight tonnage in the coming decades. The occurrence of derailments can lead to major network disruption, significant financial losses, damage to infrastructure and rolling stock assets, environmental damage, and possibly fatalities and injuries. Defects in rolling stock wheelsets can potentially result in severe derailments if left to grow to a critical level. Rolling stock wheelsets are maintained using preventative maintenance techniques. Predictive maintenance solutions prevent unexpected failure, boost operational efficiency, and lower costs. The railway industry has been looking into the development of advanced and effective condition monitoring with a low capital cost for the online and real-time assessment of the rolling stock wheels' structural integrity and subcomponents (wheels, bearings, brakes and suspension). Existing wayside measurement systems are based on different technologies, including hot boxes, acoustic arrays, wheel impact load detectors, etc. However, significant flaws, especially bearing failures, are challenging to identify. Hot boxes can only detect bad bearings after they overheat. This indicates that the bearing has failed and will be seized soon. The combination of acoustic emission (AE) and vibration analysis has been used in this study to identify wheelset defects, particularly in wheels and axle bearings. Based on the new approach and thanks to the capability of early fault detection, predictive maintenance methods can be effectively applied whilst minimising the risk of catastrophic failure and reducing the level of disruption to an absolute minimum. The present study looked into the quantitative evaluation of damage in axle bearings using an advanced customised vibroacoustic remote condition monitoring system developed at the University of Birmingham to improve the early fault detectability in in-service rolling stock wheelsets and improve maintenance planning. Laboratory tests using AE sensors and accelerometers were conducted to compare the sensitivity of each technique and evaluate the synergy in combining them. An experiment using the Amsler machine and bearing test rig proved that raw data and Fast Fourier transform (FFT) are inefficient for defect detection. More advanced signal processing techniques, including Kurtosis, were also applied to find the ideal core frequency and bandwidth for a band-pass filter. Cepstral analysis determines the complex natural logarithm of data's Fourier transform, and the power spectrum's inverse Fourier transform. It helps identify the bearing defect's harmonics from vibration measurement. High-frequency harmonics arising from wheel and axle bearing faults were proven to be detectable from the acquired AE signals. The trial at Bescot yard demonstrates wayside measurement using a compact data acquisition system. Kurtogram-based band-pass filters eliminate environmental and undesired vibrations. The filtered signal with a better signal-to-noise ratio has less noise than the original signal. Another real-world wayside measurement was conducted at the Cropredy site to demonstrate train and wheelset defect detection

High temperature electromagnetic acoustic transducer for guided wave testing

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis research focuses on the theoretical analysis, development and experimental evaluation of a water cooled Electromagnetic Acoustic Transducer (EMAT) specifically designed for high-temperature Guided Wave Testing (GWT). Its novel design and detailed calculation of its optimum operating conditions resulted in its effectiveness at high temperatures for both short (500oC) and long-term inspection (250oC). All the steps followed for the theoretical and experimental investigation of the limitations of the existing technology and the development of a probe that can overcome these boundaries are presented. Finite Element Analysis (FEA) was performed for the optimization of the EMAT design and estimation of its ultrasonic and thermal properties at room and high temperatures over time. The wave mode purity profile of the Periodic Permanent Magnet (PPM) EMAT was theoretically studied as well as the effect of temperature rise on its ultrasonic performance. Thermal and Computational Fluid Dynamics (CFD) analysis was accomplished for the EMAT design optimization and calculation of its optimum operating conditions. The experimental validation of the theoretical study was also accomplished. The novel water cooled EMAT was developed and experimentally evaluated regarding its ultrasonic and thermal response at room and high temperatures. An empirical method for the enhancement of EMAT performance and its SNR was established. The wave mode purity characteristics of PPM EMAT were experimentally investigated via Laser vibrometry tests, which agreed with FEA results. The impedance analysis and ultrasonic evaluation of the EMAT at both room and high temperatures against various operating conditions were linked and compared to the results obtained from the ultrasonic, thermal and fluid FEA. In all cases, the experimental study is in good agreement with the theoretical results

Strobo-strain: Stroboscopic neutron and X-ray strain measurements in dynamically loaded engineering components

Increasing awareness of the impact that fossil fuel power generation has taken on the environment has driven intensive exploration of low carbon alternatives, such as wind energy. Whilst wind power generation has become a growing resource for meeting the demands of ever- increasing global energy consumption, wind technology has demonstrated reliability issues, mainly due to the stochastic nature of wind conditions. Consequently, downtime associated with unexpected component failure is negatively affecting profit for wind turbine operators. A major contributor towards wind turbine downtime is the premature failure of gearbox bearings, and whilst typically many factors can contribute towards accelerated damage, overloading is believed to be a key driver. Overload events occur due to inertial effects within the drive train, with plastic deformation in the static bearing raceway suggested to be a significant influence on damage propagation, however the mechanism by which this occurs is relatively unknown. Non-destructive testing approaches, for example synchrotron X-ray and neutron diffraction techniques, have demonstrated the potential for characterising damage in engineering components, and with further development offer the potential for investigating the failure initiation mechanism present in the aforementioned wind turbine bearing and other similar components. This project therefore focuses on the development of such techniques, specifically looking at the advancement of stroboscopic diffraction and neutron imaging methods, taking the wind turbine gearbox bearing as an exemplar component for this study. A novel stroboscopic technique has been incorporated into a custom-built bearing rig, permitting the measurement of time-resolved subsurface strains in dynamic bearings. Prior to testing, bearing samples were exposed to significant overloading, with the aim of reducing experimental times to those appropriate for neutron and X-ray investigations, whilst also creating a specific location to be examined that is more prone to damage. The stroboscopic technique was used to successfully measure dynamic subsurface strain when contact stresses were at a maximum magnitude, whereby the rolling element was in contact with the overloaded region. Additionally, the benefit of using eventmode data acquisition during the neutron diffraction experiment, demonstrated the capability of stroboscopic neutron diffraction for analysing cyclic strains associated with rolling contact fatigue. Neutron imaging methods for damage characterisation are also being explored, with neutron Bragg edge transmission imaging becoming an increasingly popular technique for measuring throughaveraged elastic strains. To aid development of this technique for the purpose of evaluating damage, an in situ fatigue experiment was performed, whereby crack nucleation and propagation in a notched sample was successfully detected. Neutron computed tomography was also applied postfatigue, successfully permitting visualisation of the crack. Having managed to evaluate elastic strain using this method, Bragg edge transmission imaging was then performed on a bearing sample at increasing load. The Bragg edge broadening parameter presented notable increases beneath the contact, indicative of material yielding, allowing for a qualitative estimation of subsurface plastic zone evolution, as predicted with finite element modelling. The non-destructive neutron imaging results were compared with post-mortem micromechanical characterisation such as scanning electron microscopy to validate the findings. The combined neutron and X-ray diffraction, neutron imaging, finite element analysis and micromechanical characterisation of damaged bearings resulted in improved understanding of the bearing failure mechanism, which can be exploited in the future to improve bearing performance and reliability

A Digital Triplet for Utilizing Offline Environments to Train Condition Monitoring Systems for Rolling Element Bearings

Manufacturing competitiveness is related to making a quality product while incurring the lowest costs. Unexpected downtime caused by equipment failure negatively impacts manufacturing competitiveness due to the ensuing defects and delays caused by the downtime. Manufacturers have adopted condition monitoring (CM) techniques to reduce unexpected downtime to augment maintenance strategies. The CM adoption has transitioned maintenance from Breakdown Maintenance (BM) to Condition-Based Maintenance (CbM) to anticipate impending failures and provide maintenance actions before equipment failure. CbM is the umbrella term for maintenance strategies that use condition monitoring techniques such as Preventive Maintenance (PM) and Predictive Maintenance (PdM). Preventive Maintenance involves providing periodic checks based on either time or sensory input. Predictive Maintenance utilizes continuous or periodic sensory inputs to determine the machine health state to predict the equipment failure. The overall goal of the work is to improve bearing diagnostic and prognostic predictions for equipment health by utilizing surrogate systems to generate failure data that represents production equipment failure, thereby providing training data for condition monitoring solutions without waiting for real world failure data. This research seeks to address the challenges of obtaining failure data for CM systems by incorporating a third system into monitoring strategies to create a Digital Triplet (DTr) for condition monitoring to increase the amount of possible data for condition monitoring. Bearings are a critical component in rotational manufacturing systems with wide application to other industries outside of manufacturing, such as energy and defense. The reinvented DTr system considers three components: the physical, surrogate, and digital systems. The physical system represents the real-world application in production that cannot fail. The surrogate system represents a physical component in a test system in an offline environment where data is generated to fill in gaps from data unavailable in the real-world system. The digital system is the CM system, which provides maintenance recommendations based on the ingested data from the real world and surrogate systems. In pursuing the research goal, a comprehensive bearing dataset detailing these four failure modes over different collection operating parameters was created. Subsequently, the collections occurred under different operating conditions, such as speed-varying, load-varying, and steadystate. Different frequency and time measures were used to analyze and identify differentiating criteria between the different failure classes over the differing operating conditions. These empirical observations were recreated using simulations to filter out potential outliers. The outputs of the physical model were combined with knowledge from the empirical observations to create ”spectral deltas” to augment existing bearing data and create new failure data that resemble similar frequency criteria to the original data. The primary verification occurred on a laboratory-bearing test stand. A conjecture is provided on how to scale to a larger system by analyzing a larger system from a local manufacturer. From the subsequent analysis of machine learning diagnosis and prognosis models, the original and augmented bearing data can complement each other during model training. The subsequent data substitution verifies that bearing data collected under different operating conditions and sizes can be substituted between different systems. Ostensibly, the full formulation of the digital triplet system is that bearing data generated at a smaller size can be scaled to train predictive failure models for larger bearing sizes. Future work should consider implementing this method for other systems outside of bearings, such as gears, non-rotational equipment, such as pumps, or even larger complex systems, such as computer numerically controlled machine tools or car engines. In addition, the method and process should not be restricted to only mechanical systems and could be applied to electrical systems, such as batteries. Furthermore, an investigation should consider further data-driven approximations to specific bearing characteristics related to the stiffness and damping parameters needed in modeling. A final consideration is for further investigation into the scalability quantities within the data and how to track these changes through different system levels