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Bearing fault detection using machine learning on vibration and sound signals
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonA bearing is a machine element that constrains relative motion to only the desired motion
and reduces friction between moving parts, especially in high-speed trains, wind turbines,
and railway wheelset applications. As the complexity of equipment and the harshness of
operational environments in these industries increase, bearings, as critical components, are
prone to faults that can lead to significant damage and costly maintenance. Therefore, accurately
and in real-time detecting these faults is crucial for ensuring the safe and efficient
operation of equipment. This research explores modern signal processing techniques and
machine learning algorithms, integrating multimodal data such as vibration and sound, to
propose efficient fault diagnosis models, providing valuable insights for future industrial
intelligent maintenance systems.
Firstly, for fault diagnosis in high-speed train rolling bearings, this thesis introduces a
model based on frequency domain feature extraction and a Bidirectional Long Short-Term
Memory (Bi-LSTM) network. High-speed trains, operating under long periods of dynamic
load and high-frequency vibrations, are prone to bearing fatigue damage, making early
fault detection essential for safety. Traditional fault detection methods mainly focus on
time-domain signal analysis, which often fails to capture key frequency domain features.
To address this, the study first transforms time-domain signals into the frequency domain
using Fast Fourier Transform (FFT), followed by fault classification using the Bi-LSTM
model to identify fault types from both directions of sequential data. Experimental results
demonstrate that the Bi-LSTM model based on frequency domain signals significantly
improves fault classification accuracy compared to traditional methods, validating its
effectiveness and superiority.
Secondly, a real-time monitoring system for wind turbine bearing fault detection is designed
and implemented, utilizing a simplified neural network model running on a Raspberry
Pi. Wind turbine bearings are subjected to high loads and harsh environmental conditions,
and bearing faults can lead to turbine shutdowns, affecting energy production and
increasing maintenance costs. To achieve real-time monitoring and fault prediction, a
simplified neural network algorithm is designed, efficiently implemented on a Raspberry
Pi. By segmenting sensor data, the system quickly analyzes each data segment and delivers
predictions within milliseconds. This model demonstrates high fault detection efficiency
and response speed, significantly reducing maintenance costs and improving operational efficiency.
Finally, for railway wheelset bearings, this thesis proposes a new fault detection method
based on GreyWolf Optimizer (GWO) and Support Vector Machine (SVM). Wheelset bearings
in railway vehicles endure complex loads and environmental pressures, making fault
detection critical. Traditional single-source vibration signal analysis often falls short of
capturing comprehensive fault characteristics. The study innovatively integrates vibration
and sound data, providing a richer set of diagnostic information. The GWO is employed to
optimize the SVM model’s hyperparameters, resulting in a highly efficient fault detection
method. The experimental results show that the proposed approach significantly improves
fault detection performance compared to traditional models, achieving rapid and accurate
predictions, thereby minimizing downtime and maintenance costs.
Overall, this research demonstrates the potential of various machine learning and optimization
algorithms in bearing fault detection across different industrial applications.
By integrating frequency domain signal processing, neural networks, and optimization
algorithms, the thesis overcomes the limitations of traditional time-domain methods, significantly
enhancing the accuracy and real-time capabilities of fault diagnosis. Specifically,
the high-speed train bearing fault diagnosis method, based on FFT and Bi-LSTM, showcases
superior performance in handling complex frequency domain signals. The real-time
monitoring system for wind turbine bearings leverages lightweight neural networks on
embedded devices, offering high efficiency and low latency. The vibration-sound fusion
method for wheelset bearings, optimized by GWO, achieves better detection precision and
speed. Future research could explore the integration of additional sensor modalities and
combine deep learning algorithms with optimization techniques to further improve diagnostic
accuracy and application breadth. Overall, the three methods proposed in this thesis
offer effective solutions for intelligent maintenance systems and provide essential technological
support for the safe operation and maintenance of industrial equipment in the future
Development of a Web-Based e-Portal for Freeform Surfaced Lens Design and Manufacturing and Its Implementation Perspectives
Get PDFData Availability Statement:
The data presented in this study are available on request from the corresponding author due to the privacy concerns associated with the research.In modern freeform surfaced optics manufacturing, ultraprecision machining through single-point diamond turning (SPDT) plays a crucial role due to its ability to meet the high accuracy demands of optical design and stringent surface quality requirements of the final optic. The process involves meticulous steps, including optic surface modeling and analysis, optic design, machining toolpath generation, and manufacturing. This paper presents an integrated approach to customized precision design and the manufacturing of freeform surfaced varifocal lenses through a web-based e-portal. The approach implements an e-portal-driven manufacturing system that seamlessly integrates lens design, modeling and analysis, toolpath generation for ultraprecision machining, mass personalized customization, and service delivery. The e-portal is specifically designed to meet the stringent demands of personalized mass customization, and to offer a highly interactive and transparent experience for the lens users. By using Shiny and R-script programming for platform development and combining COMSOL Multiphysics for the ray tracing simulation, the e-portal leverages open-source technologies to provide manufacturing service agility, responsiveness, and accessibility. Furthermore, the integration of R-script and Shiny programming allows for advanced interactive information processing, which also enables the e-portal-driven manufacturing system to be well suited for personalized complex products such as freeform surfaced lenses.This research received no external funding
How do multinational enterprises respond to geopolitics? A review and research agenda
Get PDFSupporting Information is available online at: https://onlinelibrary.wiley.com/doi/10.1111/ijmr.12399#support-information-section .International business research emphasizes the importance of geopolitics to multinational enterprise (MNE) strategic decision-making. Yet, insights and evidence are dispersed across levels of analysis and disciplinary perspectives, making it difficult to determine how exactly MNEs respond to geopolitics. Leveraging a sample of 97 papers from journals in strategy, international business and management, we clarify the impact of geopolitics on home and host country relations and the interplay with MNE strategic decision-making. In advancing existing knowledge boundaries, we make sense of MNE responses to geopolitically induced uncertainty by accounting for MNEs’ heterogeneous political capabilities and investment real options. We show that when MNEs have strong political capabilities, they often opt for a shaping engagement response to influence home–host country relations. However, weak political capabilities limit investments with flexible real options and restrict investments with committed real options, leading to adaptive engagement, non-engagement and exit responses. Finally, we outline an agenda for future studies on the topic
Bi-level multi-objective optimization of harmonic filters for PV penetration and harmonics mitigation in power distribution using Autism-based optimizer
Get PDFThe rising usage of power electronic converters linked to renewable energy sources has become a major source of harmonics in power systems. Passive harmonic filters are an excellent solution for addressing this issue. However, these traditional filters have a problem linked to resonance frequency, which needs damping. This paper introduces a Harmonic Blocking Filter (HBF) that consists of a shunt-connected Damped Double-Tuned Passive Filter (DDTF) and a series component. Six different DDTF schemes are investigated: four single-resistor DDTFs (SR-DDTF) and two double-resistor DDTFs (DR-DDTF). This study intends to perform harmonic mitigation and increase PV penetration levels by obtaining parameters for each HBF system using the Autism-Based Optimizer (ABO). The Harmonic Pollution Factor (HPF) is a power quality indicator used to assess and reduce the system’s harmonic content. The findings show that the proposed HBF filter efficiently increases PV penetration in the system while lowering harmonic levels
Influence of sprayed water temperature on phase changes in droplets slipping in radiative flue gas flow of industrial biofuel boilers
No full textData availability:
Data will be made available on request.This work presents the investigation of phase changes of water droplets with an equivalent diameter of 50–500 μm and initial temperatures of 40 °C and 90 °C in a flue gas flow with a temperature of 200–1200 °C in the case of complex heating, modeled from the application of technological water injection in industrial biofuel boilers. It was assumed that the radiation and multiple reflection of the soot-coated walls in the two-phase flow of droplets and flue gas provide the spectral intensity of blackbody radiation incident on an individual droplet. Convective droplet heating was defined by an initial Reynolds number of 0–200. A numerical scheme based on an iterative algorithm for determining the instantaneous temperature of the droplet surface, which works according to the fastest convergence method, is based on a balance of heat fluxes. It has been highlighted that in a slipping droplet, there is an intense interaction between absorbed radiation and forced convection processes of water, which defines the complex dynamics of the droplet’s thermal and energy state in phase change regimes. It has been justified that the influence of the temperature of the injected water on the equilibrium evaporation of droplets is insignificant, but it has a significant effect on transitional phase changes in droplets. It has been suggested that the thermal state of water be defined using a parameter expressed as the ratio of the equilibrium evaporation temperature to the water temperature, rather than evaluating it based on its sensory temperature.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors
Dual beam and dual circular polarized multiplexing reflectarray for Ku band satellite communication
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All data generated or analysed during this study are included in this published article.In this letter, a broadband low-profile dual circularly polarized reflectarray (dual-CP RA) for Ku-band satellite communications is proposed. A novel single-layer metasurface unit cell consisting of a functional layer, an air layer and a metal plate is investigated first. The functional layer is a metal structure printed on the F4B substrate. The air layer can effectively extend the bandwidth, and the overall profile is only 0.12λ0, where λ0 represents the wavelength at 11.725 GHz. To independently control the phase of left-handed circularly polarized (LHCP) and right-handed circularly polarized (RHCP) waves, Dynamic phase and Berry phase methods are employed by either changing the size of microstrip lines or the rotating of the cells. Finally, a dual-CP RA with 1600 cells is designed to realize two beams at 20° for LHCP wave and − 20° for RHCP wave at 11.725 GHz. The measured gain for LHCP wave is 29.1 dB with the aperture efficiency (AE) of 47% and 1-dB gain bandwidth of 37.4%, while the gain, AE, and bandwidth for RHCP wave are 29.22 dB, 48.3% and 37% respectively.This work was supported by the Key Research and Development Program of Shaanxi under Grant 2024GX-ZDCYL-01-29 and in part by the Science and Technology Project of Xi’an City under Grant 24KPZT0010
A Real-World Case Study Towards Net Zero: EV Charger and Heat Pump Integration in End-User Residential Distribution Networks
Get PDFData Availability Statement:
The schematic diagrams for the network topology can be accessed at https://brunel.figshare.com/ (accessed on 22 September 2024), DOI 10.17633/rd.brunel.28765127. Underlying datasets for the study can be made available upon request to the authors.The electrification of energy systems is essential for carbon reduction and sustainable energy goals. However, current network asset ratings and the poor thermal efficiency of older buildings pose significant challenges. This study evaluates the impact of heat pump and electric vehicle (EV) penetration on a UK residential distribution network, considering the highest coincident electricity demand and worst weather conditions recorded over the past decade. The power flow calculation, based on Python, is performed using the pandapower library, leveraging the actual distribution network structure of the Hillingdon area by incorporating recent smart meter data from a distribution system operator alongside historical weather data from the past decade. Based on the outcome of power flow calculation, the transformer loadings and voltage levels were assessed for existing and projected heat pump and EV adoption rates, in line with national policy targets. Findings highlight that varied consumer density and diverse usage patterns significantly influence upgrade requirements.This research was partly funded by UKRI grant no. EP/Y023846/1 and partly by an International Science Partnerships Fund (ISPF) Institutional Support Grant (ODA) 2024/25 Pump priming Award
From Macro to Micro: A Lightweight Interleaved Network for Remote Sensing Image Change Detection
Get PDFAutomatic polyp segmentation is a crucial technique of computer-aided clinical diagnosis. However, some current polyp segmentation methods cannot accurately extract polyps from colonoscopy images due to the diversity of polyp shapes and sizes, as well as the blurry boundaries caused by the adhesion between polyps and surrounding tissues. To address this issue, we propose a multiscale decoupled expectation-maximization (EM) attention, namely MSD-EMA. There are two advantages of MSD-EMA. First, we design the decoupled EM attention, which decouples attention weights into the sum of pairwise term representing interregional features and unary term representing salient boundary features, thereby extracting boundary features between polyps and surrounding tissues while reducing computational complexity. Second, we propose the parallel collaborative strategy (PCS), which enables MSD-EMA to simultaneously extract sparse and dense feature maps using lower computational complexity. Sparse features are suitable for segmenting small polyps due to filtering out noise interference. Dense features are suitable for capturing large polyps that contain more location information. Comparative experiments are conducted with currently excellent polyp segmentation networks on five publicly available datasets, and the experimental results demonstrate that MSD-EMA can effectively improve polyp segmentation performance. Moreover, MSD-EMA is a plug-and-play module that can be applied to other types of segmentation tasks. The source code is available at https://github.com/EmarkZOU/MSD-EMA.10.13039/501100001809-National Natural Science Foundation of China Program (Grant Number: 62271296, 62201452, 62201334 and 62301302);
10.13039/100014472-Scientific Research Program;
10.13039/501100009103-Education Department of Shaanxi Province (Grant Number: 23JP014 and 23JP022)
Improved Automatic Deep Model for Automatic Detection of Movement Intention from EEG Signals
Get PDFData Availability Statement:
The original contributions presented in this study are included in the article material. Further inquiries can be directed to the corresponding authors. The image in Figure 1 was captured directly during the EEG data recording session of our study by the authors. It is an original image and not obtained from any external source or public database. The image is not available for public use and is restricted to this research purpose only.Automated movement intention is crucial for brain–computer interface (BCI) applications. The automatic identification of movement intention can assist patients with movement problems in regaining their mobility. This study introduces a novel approach for the automatic identification of movement intention through finger tapping. This work has compiled a database of EEG signals derived from left finger taps, right finger taps, and a resting condition. Following the requisite pre-processing, the captured signals are input into the proposed model, which is constructed based on graph theory and deep convolutional networks. In this study, we introduce a novel architecture based on six deep convolutional graph layers, specifically designed to effectively capture and extract essential features from EEG signals. The proposed model demonstrates a remarkable performance, achieving an accuracy of 98% in a binary classification task when distinguishing between left and right finger tapping. Furthermore, in a more complex three-class classification scenario, which includes left finger tapping, right finger tapping, and an additional class, the model attains an accuracy of 92%. These results highlight the effectiveness of the architecture in decoding motor-related brain activity from EEG data. Furthermore, relative to recent studies, the suggested model exhibits significant resilience in noisy situations, making it suitable for online BCI applications.This research received no external funding
Controlled, self-sustained rectilinear motion for levitated objects
Get PDF.........The research reported in this paper is supported by the Peter Jost Foundation grant "Contactless Technology for Conveying Light and Fragile Objects
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