Frequency Of Defects Research Articles

Evaluation of Alveolar Bone Destruction Patterns in the Posterior Region of the Maxilla Through Cone Beam Computer Tomography on 361 Consecutive Patients: Effect of Age and Gender.

The aim of this retrospective study is to evaluate the effect of age and gender on the alveolar bone destruction pattern with cone beam computed tomography (CBCT) in the posterior region of the maxilla. The study group included CBCT image records of 361 consecutive patients (180 males and 181 females) aged 20 years and older. Alveolar crest morphology in the maxillary right and left first and second molar teeth on retrospective images was classified as a horizontal or vertical defect (one-walled, two-walled, three-walled, and combined bone defect) on four surfaces (mesial, distal, buccal, and palatinal). Bone crater defects were defined, and furcation involvements and combined periodontal-endodontic lesions (CPELs) were placed in another category. In 361 patients, 1444 teeth were evaluated from adults between 20 and 63 years of age; 49.9% of the patients were male and 50.1% were female. Female patients had a considerably greater rate of one-walled horizontal damage in the right molar teeth than male patients (p = 0.002; p < 0.05). Patients with combined horizontal destruction in the right and left molar teeth, horizontal destruction in the palatinal, and horizontal three-walled destruction had a significantly higher mean age than patients without these periodontal destructions (p = 0.000; p < 0.05). Males were shown to have statistically higher frequencies of horizontal defects when defects were combined or distally and palatally located. Age and gender affect the alveolar bone loss pattern. Except for single-walled destructions, it has been found that the frequency of horizontal destruction increases with age. Horizontal destruction in the palatinal along with horizontal three-walled destruction increased with age.

A novel bearing weak fault diagnosis method based on rank constrained low-rank and sparse decomposition

Abstract Addressing the challenge of diagnosing incipient bearing faults amidst significant noise, a novel diagnostic approach is introduced, leveraging a Rank Constrained Low-Rank and Sparse Decomposition (RCLRSD) model tailored for weak fault detection in bearings. Initially, we raised the Autocorrelation Function of the Square Envelope in Frequency Domain (AFSEFD) as an innovative method for the estimation of fault frequencies. Subsequently, we constructed a two-dimensional observation matrix, which is formulated independently of predefined assumptions. Then, we examine the configuration and distribution patterns of bearing fault signals, uncovering the low-rank nature of fault characteristics within a designated two-dimensional transform domain. Moreover, we found the noise signal to exhibit sparsity and an approximate Gaussian distribution. Based on this, a rank-constrained low-rank sparse decomposition model is established, and rank-constrained low-rank regular constraints for feature information and sparse regular constraints and Gaussian constraints for interference signals are constructed respectively. Ultimately, we employed the Gray Wolf Optimization (GWO) algorithm to refine the model parameters, and we deduced the model's solver via the Alternating Direction Method of Multipliers (ADMM). The proposed RCLRSD model decomposes bearing fault data into three constituents: low-rank, sparse, and Gaussian components, effectively addressing the challenge of extracting weak fault signatures from bearings. The weak feature extraction capability of the RCLRSD model is verified using a multi-interference simulation model and experimental data of bearing failures under strong noise conditions; the generalization of the model is verified by the classification effect of the Support Vector Machine (SVM) under two bearing failure datasets. Comparison with various algorithms confirms the superiority of the proposed algorithm.

Defect Trends in Fire Alarm Systems: A Basis for Risk-Based Inspection (RBI) Approaches

This article presents a comprehensive statistical evaluation of defect frequency in fire alarm systems under real operating conditions, focusing on risk-based factors. The aim is not to introduce a complete RBI approach but rather to assess defect trends that can inform future RBI-based inspection strategies. The study categorizes and evaluates defects by frequency, particularly examining components such as cable and wire systems, acoustic signal devices, and the impact of detector contamination. These findings establish a foundation for developing tailored risk-based inspection and predictive maintenance strategies. A three-stage explanatory research design was employed, analyzing 4629 inspection reports with findings verified through expert surveys and cross-sample analysis. Results indicate that certain components, including acoustic devices and detectors, exhibit a significant increase in defects after 10 years, especially under challenging environmental conditions. Additionally, while ring bus technology supports less frequent functional testing, cable and wire systems require heightened attention in the early operational years. The study also identifies statistically significant trends and their potential for application to a broader system population, supporting enhanced RBI-based maintenance practices. These insights contribute to refining current maintenance approaches and offer practical recommendations for optimizing inspection routines based on risk factors. The article does not propose a system overhaul but lays essential groundwork for further research and improvement in fire alarm system reliability through targeted, risk-informed practices.

Research on the Forward Simulation and Intelligent Detection of Defects in Highways Using Ground-Penetrating Radar

The increasing variety and frequency of subgrade defects in operational highways have led to a rise in road safety incidents. This study employed ground-penetrating radar (GPR) detection and forward simulation to analyze the characteristic patterns of common subgrade defects, such as looseness, voids, and cavities. Through the integration of instantaneous feature information from different defect patterns with complex signal techniques, the boundary judgment of structural layers and anomalies in GPR images of various subgrade defects was improved. An intelligent recognition platform was established, and a radar image dataset was created and trained to evaluate the recognition performance of the You Only Look Once (YOLO) v3 and Single-Shot Multi-Box Detector (SSD) algorithms. Evaluation metrics such as precision, recall, F1-score, average precision (AP), and mean average precision (mAP) were used to assess the detection efficiency and accuracy for subgrade defect images. The results showed that YOLO v3 achieved an average detection accuracy of 76.69%, while the SSD achieved 75.07%. This study demonstrates that the reliability of the intelligent recognition and classification of highway subgrade defects can be enhanced by using GPR for non-destructive testing.

Diagnosis of incipient faults in wind turbine bearings based on ICEEMDAN–IMCKD

AbstractTo address the difficulty in extracting early fault feature signals of rolling bearings, this paper proposes a novel weak fault diagnosis method for rolling bearings. This method combines the Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and the Improved Maximum Correlated Kurtosis Deconvolution (IMCKD). Utilizing the kurtosis criterion, the intrinsic mode functions obtained through ICEEMDAN are reconstructed and denoised using IMCKD, which significantly reduces noise in the measured signal. This approach maximizes the energy amplitude at the fault characteristic frequency, facilitating fault feature identification. Experimental studies on two test benches demonstrate that this method effectively reduces noise interference and highlights the fault frequency components. Compared with traditional methods, it significantly improves the signal‐to‐noise ratio and more accurately identifies fault features, meeting the requirements for discriminating rolling bearing faults. The method proposed in this study was applied to the measured vibration signals of the gearbox bearings in the new high‐speed wire department of a Long Products Mill. It successfully extracted weak characteristic information of early bearing faults, achieving the expected diagnostic results. This further validates the effectiveness of the ICEEMDAN–IMCKD method in practical engineering applications, demonstrating significant engineering value for detecting and extracting weak impact characteristics in rolling bearings.

Low-Cost IoT-Based Sensors Dashboard for Monitoring the State of Health of Mobile Harbor Cranes: Hardware and Soft-ware Description

A cost-effective IoT-based real-time data acquisition and analysis hardware system was developed to enhance the performance of the mobile harbor cranes using a combination of a cost-effective quality control monitoring sensor dashboard (proximity sensors, angle position sensor, weight sensor, vibration sensor, and wind sensor), embedded microcontroller (Arduino), and embedded computer (Raspberry Pi). Hardware was operated using a specially developed novel Quality Control and Data Acquisition Multiprocessing software (QC-DAS). The QC-DAS can automatically collect and save real-time data of the sensors in a large-capacity SD card, monitor the state of health of the hardware, and transmit the real-time data of the sensors and the working state of the crane to an IoT server. The novelty of the QC-DAS design is that each function is encapsulated in a predefined module that is "immersed" in a message transmission medium. Modules interact by sending and receiving various signals through this medium. Modularity makes system design simpler, faster, and flexible. Thanks to modularity, users may incorporate their data processing modules when new sensors are added to match the system's needs. Thanks to modularity the DC-DAS can operate quality control hardware for any mobile cranes. There are several constraints in the quality control data acquisition system used by the Damietta Port Authority in Damietta, Egypt, SESCO TRANS company which cause the loading and unloading process to be slowed down. As a result, the SESCO TRANS company upgraded its quality control data acquisition system using the QC-DAS. The hardware was deployed for six months, during which the collected data was used to verify the crane's performance. The vibration produced by the slewing of the crane was monitored and compared with the bearing fault frequency limits, during the operation the wind speed was monitored and compared with the critical wind speed to stop the crane operation automatically, and the payloads data of the six months was collected and was used to calculate the working efficiency of the load and unload process of the crane. The results demonstrated that while maintenance costs were decreased, the crane load/unload procedure was improved. The SESCO TRANS company crane operators approved the developed approach and appreciated the achieved results.

Phase-based video vibration measurement and fault feature extraction method for compound faults of rolling bearings

Vibration characterization of rotating machinery is crucial for determining rotational and failure frequencies. Traditional contact measurement methods have limitations, while high-speed cameras offer a non-contact alternative for measuring target vibrations, spatial phase-based techniques have recently been widely used in detecting subtle vibrations and show good robustness to imaging noise. In this paper, a vision-based vibration extraction method for rotating machinery is proposed, aiming at extracting minor vibrations of bearings from them and further analyzing their fault frequencies. To address the challenge of separating a single fault frequency from the extracted compound faulty vibration signals, kurtosis is employed to analyze the inverse convolution period of multiple periodic components, and the MOMEDA filter length is optimized using the golden section algorithm. MOMEDA is then used to enhance each periodic pulse separately, and fault frequency is obtained through envelope spectrum demodulation. In the experimental part, a phase-based method is used to extract minor vibration displacements from rotor vibration video, which is subsequently compared with eddy current sensors in both time and frequency domains to verify the accuracy of the proposed method in extracting vibration displacements based on vision. Finally, vibration signals are extracted from the bearing compound fault video, and the single fault frequency characteristics of the bearing are successfully separated using the adaptive MOMEDA method, which provides an efficient and reliable method in the field of rotating machinery fault diagnosis.

Evaluating the Performance of Taiwan Airport Renovation Projects: An Application of Multiple Attributes Intelligent Decision Analysis

Performance evaluation is a vital tool for measuring whether construction projects meet their established objectives, particularly in complex tasks. It helps identify key areas for improvement and enhances resource allocation efficiency. Through precise performance evaluation, managers can make optimal decisions regarding resource use, ultimately increasing project productivity and overall performance. The objective of this study is to measure the production efficiency of airport renovation projects in Taiwan using data envelopment analysis (DEA) and to apply artificial neural networks (ANN) for predicting project quality. DEA effectively handles scenarios with multiple inputs and outputs, providing relative efficiency comparisons among projects and quantifying the potential for improvement. ANN, on the other hand, can learn nonlinear patterns from the data, allowing for accurate predictions of project quality. As construction projects become more complex, ensuring efficient operation within limited resources becomes increasingly crucial. Traditional performance evaluation methods are inadequate for addressing scenarios involving multiple inputs and outputs; therefore, using DEA and ANN offers a more accurate framework for analysis and prediction. The results of this study indicate that, through the DEA model, five projects achieved an efficiency score of 1, while twelve inefficient projects need to reduce defect frequency by 54.76% and increase the progress and budget implementation efficiency by an average of 10.33% to improve performance. The ANN model achieved a classification accuracy of 94.1% and a mean squared error (MSE) of 0.11 in regression predictions. By adopting a data-driven approach, ANN facilitates intelligent decision making and forecasting throughout the construction process. This study provides construction managers with concrete guidelines for resource allocation and quality prediction, thus enhancing project management effectiveness.

Adaptive feature mode decomposition method for bearing fault diagnosis under strong noise

In practical mechanical equipment operation, bearing vibration signals are challenging to analyze due to their non-stationarity and low signal-to-noise ratio for traditional detection methods. As a new signal decomposition method, the feature mode decomposition (FMD) method has been successfully applied to bearing fault diagnostics. However, FMD’s decomposition efficiency is easily influenced by the input parameter settings, and the efficiency of the decomposed signals is related to the number of initialized filter banks. For this reason, this paper proposes an adaptive parameterized feature mode decomposition method. Firstly, based bearing fault diagnosis method using a cuckoo search algorithm with logarithmic decline of nonlinear inertial weights and random adjustment discovery probability (DWCS), which is used to optimize the three parameters of the FMD. Then, a new approach to finding out the maximum feature fault frequency and its multiplicative feature frequency is proposed, called the feature frequency ratio (FFR), obtained from the envelope spectrum of bearing fault signal. Finally, this paper uses the DWCS based on the maximum FFR to adaptively select the best FMD parameter combination, which is verified by simulation, the results of the proposed AFMD can effectively extract bearing fault features under strong background noise with a signal-to-noise ratio of −15 dB, and actual experiments further verify the superiority of the method, which not only improves the accuracy of fault diagnosis under strong background noise, but also contributes to the overall maintenance and operational efficiency of the mechanical system.

Predicting gaps and overlaps in automated fiber placement composites by measuring sources of manufacturing process variations

Manufacturing variations in the automated fiber placement (AFP) process are one of the causes of gaps and overlaps. These manufacturing variations can be due to robot inaccuracy, tow lateral movement on the roller, tow width variation or tow compaction. An experimental setup was built to measure and investigate these various sources of manufacturing variations and their relative contributions to gap and overlap defects. This setup consisted of a commercial AFP head instrumented with additional sensors. Among all the measured sources of variations, lateral movement of the tow on the compaction roller was the biggest contributor to gaps and overlaps. The distributions of these sources of variations were fit with probability density functions. Random samples from these fits were used to simulate adjacent tows and predict the occurrence of gap and overlap defects. The distribution of predicted gaps correlated closely with the distribution of experimentally measured gaps. Thus, this approach of using statistical information about the sources of manufacturing variations to predict the frequency and magnitude of defects in a layup was validated.

Quality and Processability of Meat in Polish Native Pigs – A Review

Abstract The primary aim of pig breeding efforts is to produce animals characterized by outstanding performance, including also fattening and slaughter performance traits. However, improved carcass leanness and reduced carcass fatness, especially intramuscular fat content, have led to a deterioration of pork quality and processability. Due to the growing frequency of meat defects and the limited potential use of such meat in the production of premium products this constitutes a considerable problem for the meat industry. The breeds superior in terms of meat quality are native pig breeds, such as those kept in Poland, i.e. Pulawska (Pul), Zlotnicka Spotted (ZS) and Zlotnicka White (ZW) pigs. The aim of this paper is to present a review of research results concerning quality and processability of meat produced by the Polish native pig breeds. The presented results indicate that pigs of local breeds exhibit a low level of performance traits (daily gain, carcass leanness, backfat thickness) while maintaining very good parameters of meat quality and processability (muscle composition and physical properties, fatty acids profile, sensory traits). Thus, they are used as a source material for premium products. In view of the small size of local populations and higher production costs in Poland it is a niche production. Therefore, the current aim of breeding indigenous breeds is both to maintain a safe population size and genetic diversity and to increase popularity of their meat and processed meat products among consumers. This objective is attained, among other things, thanks to efforts to reduce cost of their production through optimization of environmental conditions and wider use of less expensive traditional feed formulations, which make it possible to fully utilize the potential of local pork resources.

Research on Typical Fault Diagnosis of Gear based on GHM Multiwavelet Transform and MCKD Algorithm

Background: Gear is the key part of the transmission part of mechanical equipment, which has a high failure rate under complex working conditions or long-term operation, directly affecting the reliability of mechanical equipment. To realize online monitoring of gears and predict gear faults, whether the diagnosis of typical gear faults is accurate and timely is a key link, so it is necessary to study the diagnosis of typical gear faults. Objective: This paper aims to propose a method that can effectively diagnose typical faults of gears and is easy to program, which is beneficial to realize online monitoring of rotating machinery equipment faults. Method: A typical gear fault diagnosis method based on GHM multiwavelet transform and maximum correlation kurtosis deconvolution algorithm (MCKD) is proposed in this paper. The measured vibration signal is decomposed into components with multiple scale spaces and frequency bands through the GHM multiwavelet transform. Then, the MCKD algorithm is run to suppress the noise of GHM multiwavelet transform coefficients, and the periodic components containing fault information are retained and reconstructed. The typical faults such as gear pitting and broken teeth are tested. Results: GHM multiwavelet transform combined with MCKD algorithm can accurately locate the frequency band containing fault information from the vibration signal with multiple modulations, double sideband, and multi-frequency interference and effectively extract the fault frequency and its frequency multiplication. The root means a square error of the extracted fault frequency is 0.08. This method has a good noise suppression effect and high accuracy in extracting fault frequency. Conclusion: The method proposed in this paper is helpful in realizing automatic programming of gear fault diagnosis and is of great significance in realizing real-time monitoring and online diagnosis of rotating machinery equipment faults. More related patents will appear in the future.

Local Mean Decomposition and Weighted Kurtosis Index for Bearing Defect Detection

Vibration signal analysis is an effective technique for detecting bearing defects, and it proposes an approach that involves signal processing methods to extract information about the defects. The initial step in the proposed approach involves dividing the signal into several components (PF) using the LMD algorithm. Subsequently, the weighted kurtosis index (WKI) values are computed, and the summation of components having WKI values higher than the average of WKI leads to the formation of a new signal containing multiple pulses that are very similar to the original signal. Then we can observe peaks at the frequencies of the bearing defects in the envelope spectrum of the new signal. Applying the proposed approach to the vibration signal available in the XJTU database shows a peak at the fault frequency of the outer ring.

Analysis of laser beam welding with superimposed 445 and 1070 nm wavelength lasers on copper by in situ synchrotron diagnostics

In laser welding, precision and reproducibility are fundamentally dependent on temporal and spatial processes of energy input. Induced by the dynamics of the melt pool, pressure equilibria in the vapor capillary, and solidification behavior, different weld seam qualities are achieved. To obtain the lowest possible defect frequency, new tailored joining strategies need to be investigated using multibeam and multiwavelength approaches. To improve the quality by influencing the process dynamics, a dual-beam approach is investigated that superimposes a stationary laser beam with a wavelength of 445 nm with a spatially modulated laser beam with a wavelength of 1070 nm. The aim is to utilize ∼10 times higher absorption of a 445 nm diode laser on copper with the high focusability of a 1070 nm fiber laser. In this context, the influence of the relative positions of the two beams to each other on the weld seam quality is investigated, while one of the beams moves either in front, behind, or coaxial to the other beam following the path of a line weld. The main objective is to observe how the laser beams influence each other and how the capillary depth and porosity vary for different parameters. To visualize the process dynamics, the welding experiments on copper are performed at the Deutsches Elektronen-Synchrotron DESY by means of in situ phase contrast videography. Quality-determining weld properties like the distribution of pores or process fluctuations are then extracted automatically from the image sequences by means of a trained neuronal network.

Noise reduction method for wind turbine gearbox vibration signals based on CVMD-DRDSAE

Abstract Wind turbine gearbox fault feature extraction is difficult due to strong background noise. To address this issue, a noise reduction method combining comprehensive learning particle swarm optimization-variational mode decomposition (CLPSO-VMD) and deep residual denoising self-attention autoencoder (DRDSAE) is proposed. Firstly, the proposed CLPSO-VMD algorithm is used to decompose the noisy wind turbine gearbox vibration signals. Subsequently, the intrinsic mode functions highly correlated with the original signals are selected through the Spearman correlation coefficient and utilized for signal reconstruction, thereby filtering out high-frequency noise outside the fault frequency band in the frequency domain characterization. Secondly, the improved DRDSAE is utilized to learn the latent representations of data in the first-level denoised signal, further reducing the residual noise within the fault frequency band while retaining important signal features. Finally, the envelope spectrum highlights the weak feature of the wind turbine gearbox vibration signal. Experimental results demonstrate the effectiveness of the proposed method in denoising wind turbine gearbox vibration signals under strong noise.

Influence of lubrication state transition on dynamic characteristics of deep groove ball bearing with localized defect in thermal environment

The accuracy of the vibration response in a defective bearing dynamics model depends on the precise representation of the lubrication friction state between the rolling element (RE) and the raceway within the model. In this study, a fault dynamic model for outer ring defect of deep groove ball bearings (DGBBs), considering the lubrication state transition in a thermal environment, is established. This model accounts for the asperity contact effect during the lubrication state transition and integrates the lubrication-friction model for temperature changes into the dynamic model when skidding occurs. The influence of lubrication state change on the fault frequency of outer ring of DGBB in thermal environment is studied. The experimental and simulation results indicate that the lubrication state of the bearing is gradually deteriorated from elastohydrodynamic lubrication to mixed lubrication with the increase of working temperature. The transformation of the lubrication state is shown to have a significant effect on friction, resulting in the fault frequency of the outer ring increasing with temperature, which exhibits substantial deviation in the thermal environment. In the temperature range of 30 °C–150 °C, the deviation of defect frequency reaches 15.5%, which affects the accuracy of bearing fault diagnosis. This study may offer recommendations for enhancing the condition monitoring of rolling bearings under extreme working conditions.

Study on the Effect of Starved Lubrication on the Dynamic Characteristics of Locally Failed Roller Bearings

When the lubricant is not enough to maintain the ideal lubrication state of the bearing, the deep groove ball bearing will enter the starved lubrication state, which leads to the change of the vibration response of the deep groove ball bearing with localized defects. Previous bearing dynamics models mainly consider the lubrication state as an ideal lubrication condition, which may not reveal the effect of starved lubrication on the vibration characteristics of defective bearings. In this paper, the internal interaction of the system under starved condition is taken into account, and a dynamic model of defective deep groove ball bearings with insufficient lubricant is established to investigate the effects of different starved lubrication degrees on the contact force, friction force, cage speed, and system vibration characteristics. The results show that the lubricant insufficiency significantly changes the contact force inside the bearing, and the increase in friction caused by the increase in the degree of starved lubrication leads to the suppression of the degree of bearing slipping. The root mean square (RMS) value of the time-domain signal and the outer ring fault frequency both increase with the increase of the starved degree. The results of the study are helpful for fault diagnosis and condition monitoring of related equipment.

Adaptive spectrum amplitude modulation method for rolling bearing fault frequency determination

Abstract Signal preprocessing and feature extraction are decisive factors in determining the frequency of bearing faults. The presence of noise interference in the status signal of rolling bearings often hampers accurate fault detection. Although there are various methods for preprocessing vibration signals in rolling bearings, they need further improvement in terms of enhancing fault feature expression and localizing fault frequency bands. This limitation significantly hinders the accuracy of fault frequency determination. In order to enhance the representation of fault information on the frequency spectrum, this study proposes a combined approach that incorporates sparse stacked autoencoder (SSAE), wavelet packet decomposition (WPD), and adaptive spectrum amplitude modulation (ASAM). The resulting method is referred to as SSAE-WPD-ASAM. Firstly, the bearing vibration signal is decomposed by wavelet packet according to the scale and frequency band of the signal. On this basis, the signal reconstruction is realized based on the wavelet packet coefficient and energy distribution in different frequency bands. Secondly, for the whole life cycle signal, the reconstructed signal is self-encoded by sparse stacked autoencoder to achieve dimensionality reduction of the reconstructed signal. Then, the spare reconstructed signal is subjected to ASAM. Finally, through envelope demodulation, peak detection of fault frequency and empirical fault frequency comparison, the specific fault types of rolling bearings are determined. The proposed method is verified by theoretical simulation and three groups of practical experiments. The results show that the proposed method has a significant improvement in diagnostic efficiency and accuracy compared with traditional diagnostic methods.

HTG transformation: an amplitude modulation method and its application in bearing fault diagnosis

Abstract Rolling bearings are critical components in modern mechanical equipment, and the health monitoring and predictive maintenance of bearings are crucial for the normal operation of machinery. Hence, there is a compelling need to delve into advanced methodologies for enhancing the detection of fault characteristics in bearings. Faulty bearings produce periodic impulses during constant-speed rotation, which can typically be detected through envelope analysis. However, in some complex conditions, the relevant fault frequencies may be hidden within interfering components. This paper presents an amplitude modulation technique called the hyperbolic tangent Gaussian (HTG) transformation, designed to extract weak fault components from signals. Firstly, a family of amplitude modulation functions, known as the HTG functions, is constructed. These functions modulate signals with normalized amplitudes to obtain a series of modulated signals. Simultaneously, a frequency domain amplitude ratio metric is used for the automatic selection of the optimal components. Finally, the HTGgram is introduced, a spectral decomposition method based on trend components, aiming to identify the best combination of filtering and modulation components. Simulations with multi-component bearing fault signals and experimental signals with composite bearing faults demonstrate that this method not only highlights fault features and suppresses noise interference but also adaptively selects frequency bands related to faults, enhancing fault information. This approach exhibits excellent adaptability and effectiveness in complex operating conditions with multiple interference components.

Lock-in spectrum: a tool for representing long-term evolution of bearing fault in the time–frequency domain using vibration signal

PurposeThis study aims to propose a method for monitoring bearing health in the time–frequency domain, termed the Lock-in spectrum, to track the evolution of bearing faults over time and frequency.Design/methodology/approachThe Lock-in spectrum uses vibration signals captured by vibration sensors and uses a lock-in process to analyze specified frequency bands. It calculates the distribution of signal amplitudes around fault characteristic frequencies over short time intervals.FindingsExperimental results demonstrate that the Lock-in spectrum effectively captures the degradation process of bearings from fault inception to complete failure. It provides time-varying information on fault frequencies and amplitudes, enabling early detection of fault growth, even in the initial stages when fault signals are weak. Compared to the benchmark short-time Fourier transform method, the Lock-in spectrum exhibits superior expressive ability, allowing for higher-resolution, long-term monitoring of bearing condition.Originality/valueThe proposed Lock-in spectrum offers a novel approach to bearing health monitoring by capturing the dynamic evolution of fault frequencies over time. It surpasses traditional methods by providing enhanced frequency resolution and early fault detection capabilities.