Vibration Signal Analysis Research Articles

Tire Pressure Monitoring System Using Feature Fusion and Family of Lazy Classifiers

ABSTRACTThe tire pressure monitoring system (TPMS) is crucial for road safety, fuel efficiency, and vehicle performance. This study focuses on nitrogen‐filled pneumatic tires due to their uniform pressure management and thermal stability advantages over air‐filled tires. Using machine learning, the research analyzes TPMS data to enhance understanding of tire behavior and vehicle safety. It employs various feature extraction methods and lazy‐based classifiers to analyze vibration signals collected under idle, high‐speed, normal, and puncture conditions using MEMS accelerometers. The study examines autoregressive moving average (ARMA), histogram, and statistical features individually and in combinations (statistical‐histogram, histogram‐ARMA, statistical‐ARMA, and statistical‐histogram‐ARMA) to improve predictive accuracy. By integrating these features, the study aims to optimize predictive modeling of TPMS. Empirically, the research achieved 97.92% accuracy using the local weighted learning (LWL) algorithm, demonstrating the effectiveness of combined statistical, histogram, and ARMA features in enhancing TPMS predictive capabilities.

Time-frequency reassignment of blade tip timing signal

The health condition of a rotor blade can be assessed by its dynamic frequency with respect the rotational speed, which can be derived from the time–frequency analysis of the blade vibration signal. Blade tip timing (BTT), as a non-contact measurement technique, provides an alternative to traditional contact strain measurement methods. However, the vibration signal measured by BTT is usually undersampled, which limits the application of conventional time–frequency analysis methods. BTT sparse time–frequency methods utilize the sparsity to reconstruct undersampled BTT signals. Nevertheless, BTT sparse time–frequency representation suffers from poor concentration and inaccurate estimation of the dynamic frequency, especially in the case of fast varying speed and limited BTT sensors. In this paper, a BTT time–frequency reassignment method is proposed to enhance the concentration of the BTT time–frequency spectrum and improve the accuracy of dynamic frequency estimation. Firstly, BTT time–frequency reassignment utilizes the blade natural frequency, obtained from the Campbell diagram, as prior information. The blade dynamic frequency with respect to the rotational speed is estimated by combining the prior frequency with the measured frequency in the Bayesian framework. Subsequently, weights are assigned to both the time–frequency coefficients and the posterior frequency. The time–frequency coefficients are relocated around the posterior frequency according to the weights. The advantages of BTT time–frequency reassignment are demonstrated through a simulation, a laboratory test and a full-scale aeroengine test. Both simulation and experimental results demonstrate that BTT time–frequency reassignment enhances the concentration of the BTT time–frequency spectrum and improves the accuracy of the estimated dynamic frequency when limited BTT sensors are available. The influence of the prior frequency is discussed to evaluate the robustness of BTT time–frequency reassignment. In the full-scale aeroengine test, the reliability of BTT time–frequency reassignment is verified under the fast varying speed.

Application of the Integral Energy Criterion and Neural Network Model for Helicopter Turboshaft Engines’ Vibration Characteristics Analysis

This article presents a vibration signal analysis method to diagnose helicopter turboshaft engine defects such as bearing imbalance and wear. The scientific novelty of the article lies in the development of a comprehensive approach to diagnosing helicopter turboshaft engine defects based on the vibration signals amplitude and frequency characteristics integral analysis combined with a neural network for probabilistic defect detection. Unlike existing methods, the proposed approach uses the energy criterion for the vibration characteristics. It averages the assessment of unique signal processing algorithms, which ensures reliable defect classification under flight vibration conditions. The method is based on representing vibration signals as a sum of harmonic oscillations supplemented by noise components, which helps to identify deviations from typical values. The developed method includes a state function in which the amplitudes and frequency characteristics from nominal parameters estimate deviations. When the critical threshold is exceeded, the function signals possible malfunctions. A multilayer neural network is used to classify defect types, providing high classification accuracy (from 0.985 to 0.994). Computer experiments on the developed seminaturalistic modeling stand confirm that the method can detect increased vibration levels, which is the potential failure indicator. Comparative analysis shows the proposed method’s accuracy and noise resistance superiority, emphasizing the importance of introducing modern technologies to improve aircraft operation reliability and safety.

Current signal analysis using SW-GAT networks for fault diagnosis of electromechanical drive systems under extreme data imbalance

Abstract In complex industrial environments, ensuring the safe operation and effective maintenance of electromechanical equipment is of paramount importance. Intelligent fault diagnosis based on deep learning is currently the most popular data-driven method. However, conventional intelligent fault diagnosis techniques face several challenges: (1) Most diagnostic models rely heavily on analyzing vibration signals. However, vibration sensors are difficult to deploy in space-constrained environments, and vibration signals are frequently contaminated by strong noise. (2) The prevalence of class imbalance between normal and fault data in equipment condition monitoring can lead to model over-reliance on information from a few classes. (3) Traditional diagnostic models presuppose data independence, neglecting the coupling relationships between data. To address the aforementioned issue, this paper proposes a Self-Weighted Graph Attention Networks (SW-GAT) based on motor stator current signal analysis, aimed at solving the fault diagnosis problem of critical transmission components in electromechanical systems under severely imbalanced data scenarios. Firstly, the raw current data is preprocessed using Stacked Auto-Encoders (SAE), and then the decoded current frequency-domain data is utilized to construct graphical data, thereby enhancing the non-common features and weak fault information in the current signals. Secondly, by introducing the graph pooling attention mechanism into GAT, the model can more effectively focus on useful fault feature information within the graph data. Finally, a novel interclass adjustment loss function is designed to adaptively adjust and balance class weights, enabling the model to pay greater attention to minority class samples and thereby improving the recognition accuracy for minority class faults. Validating the proposed method on two cases and comparing it with other advanced approaches, our method achieved the highest accuracy among the compared methods.

Locating method of buried PE pipeline based on vibration signal analysis

Abstract This paper proposes a buried PE gas pipeline positioning method based on vibration signal analysis. Firstly, the collected signal is denoised using the Dual-tree complex wavelet transform (DTCWT) method, and then the delay time of the denoised collected signal is extracted through the cross-correlation function to locate the buried PE pipeline. A simulation model is established on this theoretical basis, and sensor asymmetric and symmetric arrangements are simulated in COMSOL to observe the peak values of the collected signal in the time domain, determining the relationship between the time difference of the two signals and the pipeline position. Through simulation, it is known that the pipeline location can be judged by the magnitude of the delay time. The method proposed in this paper addresses the shortcomings of existing pipeline positioning methods and provides a basis for future pipeline positioning.

Application of the Multifractal Spectrum to the Analysis of Vibration Signals Generated During Testing of Selected Composite Materials

Application of the Multifractal Spectrum to the Analysis of Vibration Signals Generated During Testing of Selected Composite Materials

Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review

Many industrial processes, from manufacturing to food processing, incorporate rotating elements as principal components in their production chain. Failure of these components often leads to costly downtime and potential safety risks, further emphasizing the importance of monitoring their health state. Vibration signal analysis is now a common approach for this purpose, as it provides useful information related to the dynamic behavior of machines. This research aimed to conduct a comprehensive examination of the current methodologies employed in the stages of vibration signal analysis, which encompass preprocessing, processing, and post-processing phases, ultimately leading to the application of Artificial Intelligence-based diagnostics and prognostics. An extensive search was conducted in various databases, including ScienceDirect, IEEE, MDPI, Springer, and Google Scholar, from 2020 to early 2024 following the PRISMA guidelines. Articles that aligned with at least one of the targeted topics cited above and provided unique methods and explicit results qualified for retention, while those that were redundant or did not meet the established inclusion criteria were excluded. Subsequently, 270 articles were selected from an initial pool of 338. The review results highlighted several deficiencies in the preprocessing step and the experimental validation, with implementation rates of 15.41% and 10.15%, respectively, in the selected prototype studies. Examination of the processing phase revealed that time scale decomposition methods have become essential for accurate analysis of vibration signals, as they facilitate the extraction of complex information that remains obscured in the original, undecomposed signals. Combining such methods with time–frequency analysis methods was shown to be an ideal combination for information extraction. In the context of fault detection, support vector machines (SVMs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, k-nearest neighbors (KNN), and random forests have been identified as the five most frequently employed algorithms. Meanwhile, transformer-based models are emerging as a promising venue for the prediction of RUL values, along with data transformation. Given the conclusions drawn, future researchers are urged to investigate the interpretability and integration of the diagnosis and prognosis models developed with the aim of applying them in real-time industrial contexts. Furthermore, there is a need for experimental studies to disclose the preprocessing details for datasets and the operational conditions of the machinery, thereby improving the data reproducibility. Another area that warrants further investigation is differentiation of the various types of fault information present in vibration signals obtained from bearings, as the defect information from the overall system is embedded within these signals.

DIAGNOSIS OF ROLLING BEARING DEFECTS BASED ON WAVELET ANALYSIS

Purpose. Development and improvement of a method for analyzing vibration signals from rolling bearings based on wavelet analysis for the detection and identification of equipment defects. Research methods. Wavelet analysis was employed for processing vibration signals from rolling bearings. Threshold wavelet filtering was applied to highlight weak impulse components in the signals, and Morlet wavelet was used to ensure effective filtration. Results. The research results indicate that the proposed method, utilizing wavelet filtering, enhances the speed and reliability of vibration diagnostics for bearings. This allows for the efficient extraction of characteristic frequencies associated with various types of rolling bearing defects. In comparison with other signal analysis methods, the use of the developed method based on continuous wavelet analysis has proven to be particularly effective in extracting characteristic diagnostic frequencies. This method not only allows for the identification of specific types of defects in rolling bearings but also ensures universality, enabling its successful application for analyzing other types of non-stationary signals. Experimental studies have confirmed the high efficiency of the developed method, especially in the early stages of defect development. The application of this method is evident not only in its ability to effectively highlight the characteristic frequencies of bearings but also in its capacity to conduct signal analysis for the identification of equipment defects as a whole. This makes the proposed method a promising and versatile tool for the diagnosis and monitoring of the condition of technical systems. Scientific novelty. Application of the proposed method for processing vibration signals from rolling bearings based on wavelet analysis to improve the effectiveness of defect detection and identification in equipment. Practical value. The developed method can be applied in the industrial sector for the analysis and diagnostics of rolling bearings in equipment. It enables the timely detection of defects, reduces the risk of equipment failure, and lowers operational maintenance costs. Thus, this method has practical value in enhancing the reliability and productivity of industrial equipment.

Enhancing induction machine fault detection through machine learning: Time and frequency analysis of vibration signals

The integration of machine learning algorithms into fault diagnosis is regarded as an advanced and effective method for detecting electrical system faults. Vibration signals are identified as valuable and easily accessible data for fault identification in electrical machines. In this study, experiments were conducted to assemble a dataset consisting of 525 × 3 instances, each containing 200 three-axis vibration signal measurements. These measurements were obtained from a laboratory test bench equipped with a customized winding three-phase induction machine. Instead of directly analyzing the vibration signals, a feature extraction approach was employed, calculating a comprehensive set of statistical, harmonic, and impulsive features from the time domain (e.g., ShapeFactor, SINAD, ClearanceFactor, ImpulseFactor, CrestFactor, Kurtosis, Skewness, THD, Std, RMS, Mean, PeakValue, SNR), along with spectral features from the frequency domain (e.g., Peak Frequencies, Amplitudes, BandPower). To improve model efficiency, Analysis of Variance (ANOVA) was applied to select only the most significant features, with F-statistics used to rank feature importance. Features with higher F-statistics were prioritized for their ability to explain more variance in motor fault classification. This selective approach reduced model complexity, helping to minimize overfitting and focus on features that had a substantial impact on predictive accuracy. By concentrating on these high-impact features, a balance between simplicity and performance was achieved. These selected features were then used to train five machine learning classifiers: Ensemble (Bagged Trees), Quadratic Support Vector Machine, Weighted K-Nearest Neighbors, Efficient Logistic Regression, and Neural Networks. The performance of these classifiers was evaluated using various metrics, with validation accuracy rates ranging from 78.3% to 98.8%. A comparative study with similar works in the literature was conducted, demonstrating that high performances were obtained with the proposed approach while maintaining computational efficiency.

Research on faults diagnosis of dynamic balance in scroll compressor based on CWT-MViTV2

Abstract The dynamic balancing of rotor systems is a critical factor affecting the vibration of scroll compressors. Traditionally, the detection and correction of rotor dynamic balancing systems have relied on time-domain and frequency-domain analysis of vibration signals, as well as validation using rotor dynamic balancing test platforms. This paper proposes a novel fault diagnosis method for the dynamic balancing of scroll compressor rotor systems. This method acquires the time-domain vibration signals of the scroll compressor through online detection experiments. These signals are then transformed into time-frequency maps using continuous wavelet transform (CWT). By integrating the MViTV2 neural network, this approach enables effective identification of dynamic balancing fault types without requiring disassembly or shutdown of the machine. The fault types, corresponding to the offset of the balancing weight’s centroid, will provide direct data support for subsequent validation work. This study compares various neural network models combined with time-frequency maps and demonstrates that the proposed model achieves the highest accuracy of 99.749% compared to other models, and the method’s generalizability is validated in the public dataset. Furthermore, the proposed model maintains a high accuracy of 94.872% in high-noise environments. After improvement, the accuracy of the model has been increased to 95.641%, the training time and the diagnosis time has been reduced to 0.240 iter/s and 0.0277 s.

Assessing combustion characteristics of alternative fuels in a CI engine by use of a wavelet-based analysis of engine vibration signals

In the current work, a wavelet-based approach for assessing combustion performance of alternative fuels in compression-ignition engines, indicated that changes in cylinder pressure amplitude were reflected by commensurate changes in a proposed parameter, with a maximum of a 9% variation. The proposed parameter, the wavelet transform coefficient maximum amplitude (WMA), successfully accomplished this for various engine conditions. The wavelet transform coefficient standard deviation (WSD), a second proposed parameter, was able to track changes in the rates of pressure rise associated with changes in load conditions and fuel type, with a maximum variation in the parameter of 7%. The approach was assessed by testing six fuels in a test engine unit. Thermodynamic property data and vibration signal data were recorded for each of the fuels tested, at six loading conditions. Subsequently, combustion performance was evaluated using traditional thermodynamic parameters and using the WMA and WSD via a MATLAB based algorithm. Thus, it was surmised that the proposed approach can form the basis for a vibration-based assessment of alternative fuel combustion performance in reciprocating engines.

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 vibration signals near ground surface during blasting excavation of a tunnel in fractured rock

This study aims to analyze the vibration signals near the ground surface due to the underneath drilling and blasting activities in a fissured rock tunnel. Blasting induced vibration on the ground surface was continuously monitored in a fissured rock tunnel drilling and blasting excavation project in field. Wavelet packet analysis of the vibration signals using Matlab was carried out for signal denoising, differential blasting delay time interval identification, and three-way time-frequency energy analysis. The results show that within a 30 m range from the palm face, the dominant frequency bands of blasting-induced vibrations on the ground surface were concentrated in the range of 0–130 Hz. Two prominent peak frequency bands were identified at 31.25–39.063 Hz (low-frequency band) and 93.75–101.56 Hz (high-frequency band), accounting for 12% of the total energy. Among the three directions of ground surface vibrations, the energy decay was the most significant in the x-direction (tunnel excavation direction), which amounted to 54.29% of the overall energy decay with increasing distance. The energy decay within the 50–80 Hz range was the most pronounced (more than 90%), when the angle between the vibration propagation direction and the fissure or joint direction was 75°. The conclusions provide the insights in the attenuation of blast-induced vibrations in fissured rock and can potentially assist in the design of blasting vibration control.

SigBERT: vibration-based steel frame structural damage detection through fine-tuning BERT

PurposeThis article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact and impaired structures by analyzing vibration signals. Structural health monitoring (SHM) systems are crucial for identifying and locating damage in civil engineering structures. The proposed method aims to improve upon existing methods in terms of cost-effectiveness, accuracy and operational reliability.Design/methodology/approachSigBERT employs a fine-tuning process on the BERT model, leveraging its capabilities to effectively analyze time-series data from vibration signals to detect structural damage. This study compares SigBERT's performance with baseline models to demonstrate its superior accuracy and efficiency.FindingsThe experimental results, obtained through the Qatar University grandstand simulator, show that SigBERT outperforms existing models in terms of damage detection accuracy. The method is capable of handling environmental fluctuations and offers high reliability for non-destructive monitoring of structural health. The study mentions the quantifiable results of the study, such as achieving a 99% accuracy rate and an F-1 score of 0.99, to underline the effectiveness of the proposed model.Originality/valueSigBERT presents a significant advancement in SHM by integrating deep learning with a robust transformer model. The method offers improved performance in both computational efficiency and diagnostic accuracy, making it suitable for real-world operational environments.

Enhanced Bearing Fault Diagnosis in NC Machine Tools Using Dual-Stream CNN with Vibration Signal Analysis

Enhanced Bearing Fault Diagnosis in NC Machine Tools Using Dual-Stream CNN with Vibration Signal Analysis

Brake fault diagnosis using a voting ensemble of machine learning classifiers

Brake fault diagnosis is a crucial aspect of enhancing driving safety, as various faults such as air in brake fluid, brake oil spill, reservoir leak, mechanical fade and distinct types of brake pad wear can compromise vehicle safety. This study presents a method for timely fault detection by analyzing vibration signals. Vibration signals for normal and faulty conditions were captured using a hydraulic brake test setup equipped with a piezoelectric transducer and data acquisition system. Feature extraction was performed using an auto-regressive moving average (ARMA) model. The performance of five different classifiers namely, random forest (RF), Naive Bayes (NB), instance-based k-nearest neighbours (IBk), logistic model trees (LMT) and J48 decision tree was evaluated. The LMT classifier achieved the highest accuracy at 95.00 % followed by IBk, RF, J48 and NB with accuracies of 92.00 %, 90.00 %, 90.00 % and 87.00 %. To further improve the diagnosis accuracy, a voting-based ensemble approach was employed by combining two, three, four and five classifiers with the application of five different voting strategies. The results obtained showcase that a combination of three classifiers LMT, IBk and NB utilizing the majority voting rule yielded an enhanced classification accuracy of 98.00 % highlighting the effectiveness of this ensemble method in brake fault diagnosis. A similar classification accuracy of 98.00 % was achieved by four (LMT-IBk-RF-J48) and five (IBk-RF-MLP-J48-NB) classifier ensembles; however, considering the computational complexity three classifier ensembles were selected. Whilst two classifier ensembles (IBk-J48) achieved a maximum classification accuracy of 96.00 %.

Testing for finite variance with applications to vibration signals from rotating machines

In this paper we propose an algorithm for testing whether the independent observations come from finite-variance distribution. The preliminary knowledge about the data properties may be crucial for its further analysis and selection of the appropriate model. The idea of the testing procedure is based on the simple observation that the empirical cumulative even moment (ECEM) for data from finite-moments distribution tends to some constant whereas for data coming from heavy-tailed distribution, the ECEM exhibits irregular chaotic behavior. Based on this fact, in this paper we parameterize the regular/irregular behavior of the ECEM and construct a new test statistic. The efficiency of the testing procedure is verified for simulated data from three heavy-tailed distributions with possible finite and infinite variances. The effectiveness is analyzed for data represented in time domain. The simulation study is supported by analysis of real vibration signals from rotating machines. Here, the analyses are provided for data in both the time and time-frequency domains.

Wear characteristics evolution of helical gear with initial defects of bearing inner ring

This study investigated the wear characteristics and operational condition of gears with initial defects of the bearing inner ring. The working states of gears were evaluated through a combination of vibration signal analysis, wear debris concentration analysis, qualitative analysis of the wear debris, and tooth surface analysis. Based on the Hertz contact theory, a dynamic model for the gear drive system was constructed, enabling the simulation and comparison of vibration acceleration signals under normal and fault conditions. The research results revealed that bearing defects induced irregularities in vibration frequency and amplitude, adversely affecting the performance and stability of the gear system. Furthermore, these defects trigger the creation of substantial wear debris, which exacerbates gear wear and reduces the gear lifespan by about 15%. Additionally, bearing defects induced intermittent load spikes and uneven stress distribution across tooth contacts, resulting in localized high shear stresses, material flaking, and surface spalling. Consequently, initial defects of the bearing will accelerate gear wear progression and lead to abnormal wear patterns, potentially resulting in the premature failure of the gear train system. The study investigates the underlying mechanisms of gear wear evolution influenced by these defects and offers practical guidelines for the engineering, manufacturing, and maintenance of gear systems.

Naïve Bayes algorithm for timely fault diagnosis in helical gear transmissions using vibration signal analysis

Naïve Bayes algorithm for timely fault diagnosis in helical gear transmissions using vibration signal analysis

A Critical Analysis of Design for Reduction in Vibrations of Centrifugal Impellers

Centrifugal pumps are widely used for various applications. Numerous technical practices use centrifugal pumps, particularly when consistent and adaptable pump performance is required. Still, these can have several issues, including low efficiency when operated under off-design conditions and poor capitalization performance. These frequent transport mixtures, such as liquid and gas, and pure liquids. It is a well-known fact that several problems encountered by pumps in a pumping station are related to structural instability or impeller/blade failure. Vibrations can cause catastrophic failures in the impeller, so these must be kept to a minimum while designing the impeller. Vibration, cavitation, rough running, lower-than-expected efficiency, and shorter pump life can all be traced back to unfavorable process flow conditions. Although there are some design guidelines for pump configuration, the effects of fluctuating discharge on structural stability have yet to be investigated. An additional pressing problem is the identification of time cracks. Failure can occur due to cracks much before the design loads. In this work, we aim to investigate and analyze the centrifugal impeller design consideration. Using different methods for designing and modeling, we compared significant results and found the optimal solutions in which a centrifugal impellor can be designed. This work enables us to determine the best techniques and methods to overcome the problem of vibrations produced in the centrifugal impeller. This also helps us to understand the centrifugal pump's behavior and performance to improve its efficiency. Centrifugal impellors are compared across conditions, including free, forced, damped, and undamped systems. Three successful methods for designing and modeling a centrifugal impellor are proposed using these parameters. The methods of design and analysis provide predictions of the flow fields that are highly reliable. Regarding fluid distribution and pump efficiency, computational fluid dynamics provides various solutions that may be applied to impeller design. Using fluent software, we can better comprehend the pump's resonant operation. Blade mistuning is a severe problem that can be handled using the blade tip timing and strain gauge technique. Vibration and motor current signature analysis (MCSA) is also mentioned to investigate vibrational problems with the centrifugal impeller. Several strategies are discussed to lessen or eliminate the issue of vibrations in impellers. The limitations and disadvantages of using these techniques are discussed. The summary of results provides significant design and improvement in centrifugal pumps in the recent past.