Search Results (27)

In rotating machines, any faults in anti-friction bearings occurring during operation can lead to failures that are unacceptable due to considerable downtime losses and maintenance costs. Hence, early fault detection is essential, and different vibration-based methods (VBMs) are explored to recognise incipient fault signatures. Based on rotordynamics, if a bearing defect causes metal-to-metal (MtM) impacts during shaft rotation, the impacts excite high-frequency resonance responses of the bearing assembly. The defect-related frequencies are modulated with the resonance responses and rely on signal demodulation for fault detection. However, the current study highlights that the bearing fault/faults may not be detected if the defect in a bearing is not causing MtM impacts nor exciting the high-frequency resonance of the bearing assembly. In a roller bearing, a localised defect may maintain persistent contact between rolling elements and raceways, thereby preventing the occurrence of impulse vibration responses. Due to contact persistence, such defects may not generate impact and may not be detected by existing VBMs, and the bearing could behave as healthy. This paper investigates such specific cases by exploring the relationship between roller-bearing defect characteristics and their potential to generate impact loads during operation. Using an experimental bearing rig, different roller and inner-race defects are presented while their fault characteristic frequencies remain undetected by the envelope analysis, fast Kurtogram, cyclic spectral coherence, and tensor decomposition methods. This study highlights the significance of both the dimension and location of defects within bearings on their detectability based on the rotordynamics concept. Further, simple roller-beam experiments are carried out to visualise and validate the reliability of the experimental observations made on the roller bearing dynamics. Full article

Industrial Vision Inspection Systems (VISs) often struggle to adapt to increasing variability of modern manufacturing due to the inherent rigidity of their hardware architectures. Although the Reconfigurable Manufacturing System (RMS) paradigm was introduced in the early 2000s to overcome these limitations, designing such reconfigurable machines remains a complex, expert-dependent, and time-consuming task. This is primarily due to the lack of structured methodologies and the reliance on trial-and-error processes. In this context, this study proposes a novel theoretical framework to facilitate the design of fully reconfigurable handling systems for VISs, with a particular focus on fixture design. The framework is grounded in Model-Based Definition (MBD), embedding semantic information directly into the 3D CAD models of the inspected product. As an additional contribution, a general hardware architecture for the inspection of axisymmetric components is presented. This architecture integrates an anthropomorphic robotic arm, Numerically Controlled (NC) modules, and adaptable software and hardware components to enable automated, software-driven reconfiguration. The proposed framework and architecture were applied in an industrial case study conducted in collaboration with a leading automotive half-shaft manufacturer. The resulting system, implemented across seven automated cells, successfully inspected over 200 part types from 12 part families and detected more than 60 defect types, with a cycle below 30 s per part. Full article

Localized defects are common in gear transmission systems and can sometimes cause serious production problems or even catastrophic accidents. To reveal the failure mechanisms and study the localized defects in gear transmission systems, a 24-degree-of-freedom (DOF) dynamic coupling model is proposed considering shafts, bearings, and gears. The dynamic characteristics of the established model when defects appear on the raceways of bearings and surfaces of gears are analyzed. It can be found in the results that the response of the established model produces periodic shocks when localized defects appear on bearings or gears through numerical analysis. Sidebands generated by fault frequencies can be detected from the frequency spectrum. Especially, bearing-localized defects on the inner race and gear surface are similar in modulation form envelope analysis, and the increase in rotating frequency leads to difficulties in distinguishing defects on bearings and gears. The established coupling dynamic model was validated through experimentation and offers a theoretical basis for the fault diagnosis of gear transmission systems. Full article

Rotating machines are vital for ensuring reliability, safety, and operational availability across various industrial sectors. Among the faults that can affect these machines, shaft misalignment is particularly critical due to its impact on other components connected to the shaft, making it a key focus for diagnostic systems. Misalignment can lead to significant energy losses, and therefore, early detection is crucial. Vibration analysis is an effective method for identifying misalignment at an early stage, enabling corrective actions before it negatively impacts equipment efficiency and energy consumption. To improve monitoring efficiency, it is essential that the diagnostic system is not only intelligent but also capable of operating in real-time. This study proposes a methodology for diagnosing shaft misalignment faults by combining wavelet transform for feature extraction and transfer learning for fault classification. The accuracy of the proposed soft real-time solution is validated through a comparison with other time-frequency transformation techniques and transfer learning networks. The methodology also includes an experimental procedure for simulating misalignment faults using a laser measurement tool. Additionally, the study evaluates the thermal impacts and vibration signature of each type of misalignment fault through multi-sensor monitoring, highlighting the effectiveness and robustness of the approach. First, wavelet transform is used to obtain a good representation of the signal in the time-frequency domain. This step allows for the extraction of key features from multi-sensor vibration signals. Then, the transfer learning network processes these features through its different layers to identify the faults and their severity. This combination provides an intelligent decision-support tool for diagnosing misalignment faults, enabling early detection and real-time monitoring. The proposed methodology is tested on two datasets: the first is a public dataset, while the second was created in the laboratory to simulate shaft misalignment using a laser alignment tool and to demonstrate the effect of this defect on other components through thermal imaging. The evaluation is carried out using various criteria to demonstrate the effectiveness of the methodology. The results highlight the potential of implementing the proposed soft real-time solution for diagnosing shaft misalignment faults. Full article

This paper presents the design and development of a proof of concept (PoC) open-source data logger system for wireless data acquisition via Wi-Fi aimed at bench testing and fault detection in combustion and electric engines. The system integrates multiple sensors, including accelerometers, microphones, thermocouples, and gas sensors, to monitor critical parameters, such as vibration, sound, temperature, and CO₂ levels. These measurements are crucial for detecting anomalies in engine performance, such as ignition and combustion faults. For combustion engines, temperature sensors detect operational anomalies, including diesel engines operating beyond the normal range of 80 °C to 95 °C and gasoline engines between 90 °C and 110 °C. These readings help identify failures in cooling systems, thermostat valves, or potential coolant leaks. Acoustic sensors identify abnormal noises indicative of issues such as belt misalignment, valve knocking, timing irregularities, or loose parts. Vibration sensors detect displacement issues caused by engine mount failures, cracks in the engine block, or defects in pistons and valves. These sensors can work synergistically with acoustic sensors to enhance fault detection. Additionally, CO₂ and organic compound sensors monitor fuel combustion efficiency and detect failures in the exhaust system. For electric motors, temperature sensors help identify anomalies, such as overloads, bearing problems, or excessive shaft load. Acoustic sensors diagnose coil issues, phase imbalances, bearing defects, and faults in chain or belt systems. Vibration sensors detect shaft and bearing problems, inadequate motor mounting, or overload conditions. The collected data are processed and analyzed to improve engine performance, contributing to reduced greenhouse gas (GHG) emissions and enhanced energy efficiency. This PoC system leverages open-source technology to provide a cost-effective and versatile solution for both research and practical applications. Initial laboratory tests validate its feasibility for real-time data acquisition and highlight its potential for creating datasets to support advanced diagnostic algorithms. Future work will focus on enhancing telemetry capabilities, improving Wi-Fi and cloud integration, and developing machine learning-based diagnostic methodologies for combustion and electric engines. Full article

Rotary machines commonly use rolling element bearings to support rotation of the shafts. Most machine performance imperfections are related to bearing defects. Thus, reliable bearing condition monitoring systems are critically needed in industries to provide early warning of bearing fault so as to prevent machine performance degradation and reduce maintenance costs. The objective of this paper is to develop a smart monitoring system for real-time bearing fault detection and diagnostics. Firstly, a smart sensor-based data acquisition (DAQ) system is developed for wireless vibration signal collection. Secondly, a modified variational mode decomposition (MVMD) technique is proposed for nonstationary signal analysis and bearing fault detection. The proposed MVMD technique has several processing steps: (1) the signal is decomposed into a series of intrinsic mode functions (IMFs); (2) a correlation kurtosis method is suggested to choose the most representative IMFs and construct the analytical signal; (3) envelope spectrum analysis is performed to identify the representative features and to predict bearing fault. The effectiveness of the developed smart sensor DAQ system and the proposed MVMD technique is examined by systematic experimental tests. Full article

Data-based equipment fault detection and diagnosis is an important research area in the smart factory era, which began with the Fourth Industrial Revolution. Steel manufacturing is a typical processing industry, and efficient equipment operation can improve product quality and cost. Steel production systems require precise control of the equipment, which is a complex process. A gearbox transmits power between shafts and is an essential piece of mechanical equipment. A gearbox malfunction can cause serious problems not only in production, quality, and delivery but in safety. Many researchers are developing methods for monitoring gearbox condition and for diagnosing failures in order to resolve problems. In most data-driven methods, the analysis data set is derived from a distribution of identical data with failure mode labels. Industrial sites, however, often collect data without information on the failure type or failure status due to varying operating conditions and periodic repair. Therefore, the data sets not only include frequent false alarms, but they cannot explain the causes of the alarms. In this paper, a framework called the Reduced Lagrange Method (R-LM) periodically assigns pseudolabels to vibration signals collected without labels and creates an input data set. In order to monitor the status of equipment and to diagnose failures, the input data set is fed into a supervised learning classifier. To verify the proposed method, we build a test rig using motors and gearboxes that are used on production sites in order to artificially simulate defects in the gears and to operate them to collect vibration data. Data features are extracted from the frequency domain and time domain, and pseudolabeling is applied. There were fewer false alarms when applying R-LM, and it was possible to explain which features were responsible for equipment status changes, which improved field applicability. It was possible to detect changes in equipment conditions before a catastrophic failure, thus providing meaningful alarm and warning information, as well as further promising research topics. Full article

A study on the gearbox (speed reducer) defect detection models built from the raw vibration signal measured by a triaxial accelerometer and based on convolutional neural networks (CNNs) is presented. Gear faults such as localized pitting, localized wear on helical pinion tooth flanks, and lubricant low level are under observation for three rotating velocities of the actuator and three load levels at the reducer output. A deep learning approach, based on 1D-CNN or 2D-CNN, is employed to extract from the vibration image significant signal features that are used further to identify one of the four states (one normal and three defects) of the system, regardless of the selected load level or the speed. The best-performing 1D-CNN-based detection model, with a testing accuracy of 98.91%, was trained on the signals measured on the Y axis along the reducer input shaft direction. The vibration data acquired from the X and Z axes of the accelerometer proved to be less relevant in discriminating the states of the gearbox, the corresponding 1D-CNN-based models achieving 97.15% and 97% testing accuracy. The 2D-CNN-based model, built using the data from all three accelerometer axes, detects the state of the gearbox with an accuracy of 99.63%. Full article

Currently, most chemical transmission equipment relies on bearings to support rotating shafts and to transmit power. However, bearing defects can lead to a series of failures in the equipment, resulting in reduced production efficiency. To prevent such occurrences, this paper proposes an improved bearing defect detection algorithm based on YOLOv5. Firstly, to mitigate the influence of the similarity between bearing defects and non-defective regions on the detection performance, gamma transformation is introduced in the preprocessing stage of the model to adjust the image’s grayscale and contrast. Secondly, to better capture the details and semantic information of the defects, this approach incorporates the ResC2Net model with a residual-like structure during the feature-extraction stage, enabling more nonlinear transformations and channel interaction operations so as to enhance the model’s perception and representation capabilities of the defect targets. Additionally, PConv convolution is added in the feature fusion part to increase the network depth and better capture the detailed information of defects while maintaining time complexity. The experimental results demonstrate that the GRP-YOLOv5 model achieves a mAP@0.5 of 93.5%, a mAP@0.5:0.95 of 52.7%, and has a model size of 25 MB. Compared to other experimental models, GRP-YOLOv5 exhibits excellent performance in bearing defect detection accuracy. However, the model’s FPS (frames per second) performance is not satisfactory. Despite its small size of 25 MB, the processing speed is relatively slow, which may have some impact on real-time or high-throughput applications. This limitation should be considered in future research and in the optimization efforts to improve the overall performance of the model. Full article

Early detection of faults in wind energy systems can reduce downtime, operating, and maintenance costs while increasing productivity. This paper proposes a method based on the analysis of generator stator current signals to detect faults in a wind turbine gearbox equipped with a doubly fed induction generator (DFIG). A localized parameter model was established to simulate the vibratory response of a two-stage gear system under healthy and faulty conditions. The simulation was performed in the MATLAB/Simulink environment. The results include a detailed analysis of the mechanical part of the gearbox, highlighting mesh stiffness, output speed, and accelerations. Additionally, the electrical part was evaluated based on the current supplied by the doubly fed induction generator. The results were presented in the case of healthy gears and in the presence of faults such as a broken or cracked tooth. Fast Fourier transform (FFT) analysis was employed to detect gear defects in the stator current signal. The presence of a crack or broken tooth in the gearbox induces modulation of the DFIG stator current signals according to the shaft frequencies corresponding to the faulty gear. These findings provide a preliminary basis for the detection and diagnosis of this type of failure. Full article

The coal mine wind shaft is an important ventilation channel in coal mines, and it is of great significance to ensure its long-term safety. At present, the inspection of wind shafts still depends on manual work, which has low reliability and high risk. There are two main problems in the shaft wall detection of ventilation shafts: (1) The humidity and dust concentration in ventilation shafts are high, which makes imaging difficult; (2) the cracks on the shaft wall are long and irregular, so it is impossible to acquire the information of the whole crack from a single photo. Firstly, the mapping analysis between the concentration of water vapor and dust in the wind shaft and the image definition is determined by experiments. Then, the inspection robot is designed to move along the axial and circumferential directions to get close to the shaft wall, and the rack-and-rail drive design is adopted to ensure the real-time position feedback of the robot. Then, through the crack parameter detection method based on depth learning, the movement direction of the robot is controlled according to the crack direction so as to ensure that the complete crack parameters are obtained. Finally, the crack detection algorithm is verified by experiments. Full article

The detection of cracked railway axles by processing vibratory signals measured during operation is the focus of this study. The rotodynamic theory is applied to this specific purpose but, in practice and for real systems, there is no consensus on applying the results obtained from theory. Finding reliable patterns that change during operation would have advantages over other currently applied methods, such as non-destructive testing (NDT) techniques, because data between inspections would be obtained during operation. Vibratory signal processing techniques in the time-frequency domain, such as wavelet packet transform (WPT), have proved to be reliable to obtain patterns. The aim of this work is to develop a methodology to select the optimal function associated with the WPT, the mother wavelet (MW), and to find diagnostic patterns for cracked railway axle detection. In previous related works, the Daubechies 6 MW was commonly used for all speed/load conditions and defects. In this work, it was found that the Symlet 9 MW works better, so a comparative study was carried out with both functions, and it was observed that the success rates obtained with Daubechies 6 are improved when using Symlet 9. Specifically, defects above 16.6% of the shaft diameter were reliably detected, with no false alarms. To validate the proposed methodology, experimental vibratory signals of a healthy scaled railway axle were obtained and then the same axle was tested with a transverse crack located close to a section change (where this type of defect typically appears) for nine different crack depths. Full article

To address the problem of low efficiency for manual detection in the defect detection field for metal shafts, we propose a deep learning defect detection method based on the improved YOLOv5 algorithm. First, we add a Convolutional Block Attention Module (CBAM) mechanism layer to the last layer of the backbone network to improve the feature extraction capability. Second, the neck network introduces the Bi-directional Feature Pyramid Network (BiFPN) module to replace the original Path-Aggregation Network (PAN) structure and enhance the multi-scale feature fusion. Finally, we use transfer learning to pre-train the model and improve the generalization ability of the model. The experimental results show that the method achieves an average accuracy of 93.6% mAP and a detection speed of 16.7 FPS for defect detection on the dataset, which can identify metal shaft surface defects quickly and accurately, and is of reference significance for practical industrial applications. Full article

Machine health monitoring of rotating mechanical systems is an important task in manufacturing engineering. In this paper, a system for analyzing and detecting mounting defects on a rotating test rig is developed. The test rig comprises a slender shaft with a central disc, supported symmetrically by oscillating ball bearings. The shaft is driven at constant speed (with tiny variations) through a timing belt. Faults, such as the translation of the central disc along the shaft, the disc eccentricity, and defects on the motor reducer position or timing belt mounting position, are imposed. Time and frequency domain features, extracted from the vibration signal, are used as predictors in fault detection. This task is modeled as a multi-class classification problem, where the classes correspond to eight health states: one healthy and seven faulty. Data analysis, using unsupervised and supervised algorithms, provides significant insights (relevance of features, correlation between features, classification difficulties, data visualization) into the initial dataset, a balanced one. The experiments are performed using classifiers from MATLAB and six feature sets. Quadratic SVM achieves the best performance: 99.18% accuracy for the set of all 41 features extracted from X and Y accelerometer axes, and 98.93% accuracy for the subset of the 18 most relevant features. Full article

In order to solve the problems of low detection efficiency and safety of artificial surface defects in hot-state cross wedge rolling shaft production line, a machine vision-based method for detecting surface hollow defect of hot-state shafts is proposed. Firstly, by analyzing the high reflective properties of the metal shaft surface, the best lighting method was obtained. And by analyzing the image contrast between image foreground and image background, the most suitable optical filter type in image acquisition was determined. Then, Fourier Gaussian low-pass filtering method is used to remove the interference noise of rolled shafts surface in frequency domain, such as high-light, oxide skin and surface texture. Finally, by analyzing the characteristics of the surface hollow defect area, a defect identification method combining the Otsu threshold method and the adaptive threshold method is proposed to realize the effective extraction of surface hollow defect of rolled shafts. The test results show that the average recognition rate of the method based on machine vision is 95.7%. The results of this paper provide technical support to meet the production requirements of high quality and high performance of cross wedge rolling. Full article