Vibration-based Condition Monitoring Research Articles

Alpha Stable Distribution Based Morphological Filter for Bearing and Gear Fault Diagnosis in Nuclear Power Plant

Gear and bearing play an important role as key components of rotating machinery power transmission systems in nuclear power plants. Their state conditions are very important for safety and normal operation of entire nuclear power plant. Vibration based condition monitoring is more complicated for the gear and bearing of planetary gearbox than those of fixed-axis gearbox. Many theoretical and engineering challenges in planetary gearbox fault diagnosis have not yet been resolved which are of great importance for nuclear power plants. A detailed vibration condition monitoring review of planetary gearbox used in nuclear power plants is conducted in this paper. A new fault diagnosis method of planetary gearbox gears is proposed. Bearing fault data, bearing simulation data, and gear fault data are used to test the new method. Signals preprocessed using dilation-erosion gradient filter and fast Fourier transform for fault information extraction. The length of structuring element (SE) of dilation-erosion gradient filter is optimized by alpha stable distribution. Method experimental verification confirmed that parameter alpha is superior compared to kurtosis since it can reflect the form of entire signal and it cannot be influenced by noise similar to impulse.

Experimental Shock and Vibration Analysis

The analysis of shock and vibration responses is the starting point to investigate the dynamic properties of a system, a structure, or a mechanism, as well as its expected response to a given excitation (harmonic, random, shock, etc.). By this approach it can be determined whether a particular system, structure, or mechanism will fulfill its intended function; in addition, the results of the dynamic loadings acting on a structure can be predicted, such as dynamic stresses, fatigue life, and noise levels, so that its usefulness can bemaintained andmaximized. Furthermore, the analysis allows seeking those structural parameters most affecting the dynamic response so that if any improvement or change in the response is required, the structure can be modified in the most economic and appropriate way. The methods also made important great strides. Indeed, the vibratory analysis is not one simple complementary tool. It is the basis of many powerful techniques which make it possible, for example, to probe the structures or the health monitoring of materials during their service, to detect the defects and the damage, and to follow their evolution in real-time.The shock and vibration analyses are massively present today in the various branches of industry, from aeronautics to car manufacturing and from machining and maintenance to civil engineering, to mention a few areas, which have made this special issue a true need. This scientific topic leading industrial and academic researchers’ communities concerned by experimental shock and vibration analysis has highly advanced taking positive steps in recent years to develop more comprehensive and rational procedures for dynamic assessment. Starting from forty-eight submitted papers, only seventeen papers on modal analysis, vibration-based condition monitoring, damage detection and localization, experimental/numerical combined approach, physical experiments with advanced computational methods investigations, and their various engineering/industrial applications have been selected and included in this special issue after completing a careful and rigorous peer review process by the international scientific committee. Although these papers do not make an exhaustive treatment of the entire experimental shock and vibration analysis topics, they reflect different interesting issues that have constituted important points of concern for researchers in this area. We trust that this document will contribute to disseminate the current trends and topics of interest in this fascinating and fast growing area and that it will be a valuable tool in research and professional activity.

Fault diagnosis of helical gear box using naïve Bayes and Bayes net

Gears are one of the vital transmission elements, finding numerous applications in small, medium and large machinery. The vibration signals of a rotating machine contain dynamic information about its health condition. There are many articles in the literature reporting the suitability of vibration signals for fault diagnosis applications. Many of them are based on FFT, and have their own drawbacks with non-stationary signals like the ones from gears. Hence, there is a need for the development of new methodologies to infer diagnostic information from such signals. This paper uses the vibration signals acquired from gears in good and simulated faulty conditions for the purpose of fault diagnosis through the machine learning approach. A vibration-based condition monitoring system is presented for the helical gear box as it plays a relatively critical role in most of the industries. This approach has mainly three steps, namely, feature extraction, classification, and comparison of classification. This paper presents the use of the naive Bayes algorithm and Bayes net algorithm, for fault diagnosis through statistical features extracted from the vibration signals of good and faulty components of the helical gear box.

Using Johnson Distribution for Automatic Threshold Setting in Wind Turbine Condition Monitoring System

Abstract : Setting optimal alarm thresholds in vibration based condition monitoring system is inherently difficult. There are no established thresholds for many vibration based measurements. Most of the time, the thresholds are set based on statistics of the collected data available. Often times the underlying probability distribution that describes the data is not known. Choosing an incorrect distribution to describe the data and then setting up thresholds based on the chosen distribution could result in sub-optimal thresholds. Moreover, in wind turbine applications the collected data available may not represent the whole operating conditions of a turbine, which results in uncertainty in the parameters of the fitted probability distribution and the thresholds calculated. In this study Johnson distribution is used to identify shape, location, and scale parameters of distribution that can best fit vibration data. This study shows that using Johnson distribution can eliminate testing or fitting various distributions to the data, and have more direct approach to obtain optimal thresholds. To quantify uncertainty in the thresholds due to limited data, implementations with bootstrap method and Bayesian inference are investigated.

Estimation of Bogie Performance Criteria Through On-Board Condition Monitoring


 
 
 In this paper, bogie performance criteria are reviewed and it is shown that a real-time, on-board condition monitoring system can efficiently monitor these criteria to improve failure mode detection in freight rail operations. Although the dynamics of rail car bogie performance are well understood in the industry, this topic has recently received renewed attention through impending regulatory changes. These changes seek to extend empty rail car performance criteria to include loaded rail cars as well. Currently, the monitoring of bogie performance is primarily accomplished by wayside detection systems in North America. These systems are only sparsely deployed in the track network and do not offer the ability to monitor bogies continuously. The lack of these elements leads to unexpected downtimes resulting in costly reactive maintenance and lengthy periods of time before an adequate performance history can be established. This paper reviews performance criteria which critically influence bogie performance and proposes a vibration based condition monitoring strategy to estimate system component deterioration and their contribution to the development of bogie hunting. The strategy addresses both sensing techniques and monitoring algorithms to maximize the efficiency of the monitoring solution. In particular it is proposed that understanding the relation of different hunting modes to car body oscillations can be used for a deeper understanding of the rail car condition which current technologies are not able to provide.
 
 

Dynamic modelling of a one-stage spur gear system and vibration-based tooth crack detection analysis

For the purpose of simulation and vibration-based condition monitoring of a geared system, it is important to model the system with an appropriate number of degrees of freedom (DOF). In earlier papers several models were suggested and it is therefore of interest to evaluate their limitations. In the present study a 12 DOF gear dynamic model including a gyroscopic effect was developed and the equations of motions were derived. A one-stage reduction gear was modelled using three different dynamic models (with 6, 8 and 8 reduced to 6 DOF), as well as the developed model (with 12 DOF), which is referred as the fourth model in this paper. The time-varying mesh stiffness was calculated, and dynamic simulation was then performed for different crack sizes. Time domain scalar indicators (the RMS, kurtosis and the crest factor) were applied for fault detection analysis. The results of the first model show a clearly visible difference from those of the other studied models, which were made more realistic by including two more DOF to describe the motor and load. Both the symmetric and the asymmetric disc cases were studied using the fourth model. In the case of disc symmetry, the results of the obtained response are close to those obtained from both the second and third models. Furthermore, the second model showed a slight influence from inter-tooth friction, and therefore the third model is adequate for simulating the pinion׳s y-displacement in the case of the symmetric disc. In the case of the asymmetric disc, the results deviate from those obtained in the symmetric case. Therefore, for simulating the pinion׳s y-displacement, the fourth model can be considered for more accurate modelling in the case of the asymmetric disc.

Brake fault diagnosis using Clonal Selection Classification Algorithm (CSCA) – A statistical learning approach

In automobile, brake system is an essential part responsible for control of the vehicle. Any failure in the brake system impacts the vehicle's motion. It will generate frequent catastrophic effects on the vehicle cum passenger's safety. Thus the brake system plays a vital role in an automobile and hence condition monitoring of the brake system is essential. Vibration based condition monitoring using machine learning techniques are gaining momentum. This study is one such attempt to perform the condition monitoring of a hydraulic brake system through vibration analysis. In this research, the performance of a Clonal Selection Classification Algorithm (CSCA) for brake fault diagnosis has been reported. A hydraulic brake system test rig was fabricated. Under good and faulty conditions of a brake system, the vibration signals were acquired using a piezoelectric transducer. The statistical parameters were extracted from the vibration signal. The best feature set was identified for classification using attribute evaluator. The selected features were then classified using CSCA. The classification accuracy of such artificial intelligence technique has been compared with other machine learning approaches and discussed. The Clonal Selection Classification Algorithm performs better and gives the maximum classification accuracy (96%) for the fault diagnosis of a hydraulic brake system.

Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine

Roller bearings are widely used in rotating machinery and one of the major reasons for machine breakdown is their failure. Vibration based condition monitoring is the most common method for extracting some important information to identify bearing defects. However, acquired acceleration signals are usually noisy, which significantly affects the results of fault diagnosis. Wavelet packet decomposition (WPD) is a powerful method utilized effectively for the denoising of the signals acquired. Furthermore, Ensemble empirical mode decomposition (EEMD) is a newly developed decomposition method to solve the mode mixing problem of empirical mode decomposition (EMD), which is a consequence of signal intermittence. In this study a combined automatic method is proposed to detect very small defects on roller bearings. WPD is applied to clean the noisy signals acquired, then informative feature vectors are extracted using the EEMD technique. Finally, the states of the bearings are examined by labeling the samples using the hyperplane constructed by the support vector machine algorithm. The data were generated by means of a test rig assembled in the labs of the Dynamics and Identification Research Group in the mechanical and aerospace engineering department, Politecnico di Torino. Various operating conditions (three shaft speeds, three external loads and a very small damage size on a roller) were considered to obtain reliable results. It is shown that the combined method proposed is able to identify the states of the bearings effectively.

Wind turbine condition monitoring

Wind turbine condition monitoring

Combined vibration and thermal analysis for the condition monitoring of rotating machinery

Traditional practice in vibration-based condition monitoring of rotating machines with a multiple bearing system, such as turbo-generator sets, is data intensive. Since a number of sensors are required at each bearing location, the task of diagnosing faults on these systems may be daunting for even an experienced analyst. Hence, this study seeks to develop a simplified fault diagnosis method that uses just a single vibration and a single temperature sensor on each bearing. Experiments were done on a laboratory rig with two dissimilar length rotors supported through four ball bearings. Commonly encountered rotor-related faults were independently simulated and compared to a baseline condition. For reference, conventional vibration spectrum analysis was done first. Overall vibration analysis was then conducted and combined with temperature data in two diagnosis approaches. Learning from the first combined approach, which had some limitations, was used to propose a principal component analysis–based approach that was demonstrated with and without temperature data. Results of the proposed principal component analysis–based method suggest that supplementing vibration data with temperature measurements gives improved fault diagnosis when compared to fault diagnosis using vibration data alone. The experimental rig, measurements done, description of both combined approaches and results obtained are presented in this article.

Quantitative Evaluation of an FPGA based Wireless Vibration Monitoring System in relation to Different Sampling Rates

Abstract In order to achieve the high-processing performance required in typical computationally intensive high-sample rate monitoring applications, a Field Programmable Gate Array (FPGA) is often used as a hardware accelerator. Given the design complexity, increased power consumption and additional cost of an FPGA, it is desirable to determine the sampling rates for which the use of an FPGA as hardware accelerator results in most effective solution. For this purpose, a computationally intensive application is realized on an FPGA based architecture so as to determine the sampling rates for which it achieves the highest performance and consumes the least amount of energy as compared to that of a micro-controller based architecture. Based on the measured performance and energy consumption for a computationally intensive application, tri-axes/three-channel vibration based condition monitoring, the results suggest that the FPGA based architecture is the most appropriate solution for sampling frequencies of 4 kHz and above.

Vibration characteristics of single-stage planetary gear transmissions

Condition monitoring based on vibration measurement and analysis of planetary gear transmissions has not provided the same good results observed in conventional fixed-shaft gear transmissions. One of the causes being the improper interpretation of the vibration spectrum. The structure of the lines present in the spectrum of the vibrations measured on a non-faulty planetary gear transmission, with a sensor mounted on the outer part of the ring gear, is strictly related to the geometry of the transmission. Hence, different spectral patterns can be found for different planetary gear transmissions. In this work, a vibration model for a single-stage planetary gear transmission is presented to explain this phenomenon. The model is developed in the time domain and analyzed in the frequency domain by using the Fourier transform. It is shown that some planetary gear transmissions, with different geometries, present similar spectral structures. Based on this, a classification of planetary gear transmissions in four groups is proposed. The spectral characteristics of the vibrations of each group are presented. The classification allows for a fast estimation of the expected spectral pattern of the vibrations of any single-stage planetary gear transmission. Through this, contribution is expected in order to increase the effectiveness of vibrationbased condition monitoring in planetary gear transmissions.

Fault Gear Categorization: A Comparative Study on Feature Classification using Rough Set Theory and ID3

Fault diagnosis on a gear box is a difficult problem due to the non-stationary type of vibration signals it generates. Usually, one method of fault diagnosis can only inspect one corresponding fault category. Vibration based condition monitoring using machine learning methods is gaining momentum. In this paper, rough sets theory, is used to diagnose the fault gears in a gear box. Through the analysis of the final reducts generated using rough sets theory, it is shown that this method is effective for diagnosing more than one type of fault in a gear. The performance of rough set method are compared with those of the ID3 decision tree algorithm and the results prove that the rough set method has greater capability to bring out the different fault conditions of the gear box under investigation. The study reveals that the overall classification efficiency of the decision tree is to some extent better than the classification efficiency of rough sets method.

Vibration Based Fault Detection of Centrifugal Pump by Fast Fourier Transform and Adaptive Neuro-Fuzzy Inference System

Centrifugal pumps play a vital role in many critical applications and therefore continuous availability of such mechanical components become absolutely essential. This paper focuses on a problem of vibration-based condition monitoring and fault diagnosis of centrifugal pumps. The vibration based machine condition monitoring and fault diagnosis incorporate a number of machinery fault detection and diagnostic techniques. Many machinery fault diagnostic techniques utilize automatic signal classification in order to increase accuracy and reduce errors caused by subjective human judgment. This paper presented an adaptive network fuzzy inference system (ANFIS) to diagnose the fault type of the pump. The pump conditions to be considered were healthy, broken impeller, worn impeller, leakage and cavitation. These features are extracted from vibration signals using the FFT technique. The features were fed into an adaptive neurofuzzy inference system as input vectors. Performance of the system was validated by applying the testing data set to the trained ANFIS model. According to the result, total classification accuracy was 90.67%. This shows that the system has great potential to serve as an intelligent fault diagnosis system in real applications.

Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes

This paper proposes a model-based technique for detecting wear in a multistage planetary gearbox used by lifting cranes. The proposed method establishes a vibration signal model which deals with cyclostationary and autoregressive models. First-order cyclostationarity is addressed by the analysis of the time synchronous average (TSA) of the angular resampled vibration signal. Then an autoregressive model (AR) is applied to the TSA part in order to extract a residual signal containing pertinent fault signatures. The paper also explores a number of methods commonly used in vibration monitoring of planetary gearboxes, in order to make comparisons. In the experimental part of this study, these techniques are applied to accelerated lifetime test bench data for the lifting winch. After processing raw signals recorded with an accelerometer mounted on the outside of the gearbox, a number of condition indicators (CIs) are derived from the TSA signal, the residual autoregressive signal and other signals derived using standard signal processing methods. The goal is to check the evolution of the CIs during the accelerated lifetime test (ALT). Clarity and fluctuation level of the historical trends are finally considered as a criteria for comparing between the extracted CIs.This study reveals the most relevant features to be used for damage detection and condition monitoring of the gear system. It is also shown that the proposed procedure using a combination of cyclostationarity and autoregressive modeling enhance the ability to detect and diagnose mechanical wear in multi-staged planetary gears.

Improving mesh stiffness calculation of cracked gears for the purpose of vibration-based fault analysis

For the purpose of vibration-based condition monitoring and to prevent occurrence of catastrophic gear failures it is important to improve the simulated dynamic response of the studied gear model. The time-varying gear mesh stiffness will contribute to the dynamic response of a geared system. Some previously applied methods for stiffness calculation, described in the literature, show good agreement with the results obtained with FEM simulation for smaller crack sizes. However, when larger crack sizes are reached, these methods show an increasing deviation from FEM simulation results. A reduction in the gear mesh stiffness can be considered to assess the status of tooth damage and, therefore, by increasing the accuracy of the calculated mesh stiffness, dynamic simulations of a gear can be improved. In this paper a new method is presented for calculating the gear mesh stiffness for a propagating crack in the tooth root. The influence of gear mesh stiffness on the vibration-based fault detection indicators, the RMS, kurtosis and the crest factor, is investigated. Different crack sizes are examined by using this new method for sizes up to around 50% of the total tooth root thickness. When compared to FEM simulations, the presented method shows more accurate results for calculations of the gear mesh stiffness (for the studied model) than the previously suggested methods.

Dimensionality reduction via variables selection – Linear and nonlinear approaches with application to vibration-based condition monitoring of planetary gearbox

Feature extraction and variable selection are two important issues in monitoring and diagnosing a planetary gearbox. The preparation of data sets for final classification and decision making is usually a multi-stage process. We consider data from two gearboxes, one in a healthy and the other in a faulty state. First, the gathered raw vibration data in time domain have been segmented and transformed to frequency domain using power spectral density. Next, 15 variables denoting amplitudes of calculated power spectra were extracted; these variables were further examined with respect to their diagnostic ability. We have applied here a novel hybrid approach: all subset search by using multivariate linear regression (MLR) and variables shrinkage by the least absolute selection and shrinkage operator (Lasso) performing a non-linear approach. Both methods gave consistent results and yielded subsets with healthy or faulty diagnostic properties.

Condition monitoring of naturally damaged slow speed slewing bearing based on ensemble empirical mode decomposition

There have been extensive studies on vibration based condition monitoring, prognosis of rotating element bearings; and reviews of the methods on how to identify bearing fault and predict the final failure reported widely in literature. The investigated bearings commonly discussed in the literatures were run in moderate and high rotating speed, and damages were artificially introduced e.g. with artificial crack or seeded defect. This paper deals with very low rotational-speed slewing bearing (1–4.5 rpm) without artificial fault. Two real vibration data were utilized, namely data collected from lab slewing bearing subject to accelerated life test and from a sheet metal company. Empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) were applied in both lab slewing bearing data and real case data. Outer race fault (BPFO) and rolling element fault (BSF) frequencies of slewing bearing can be identified. However, these fault frequencies could not be identified using fast Fourier transform (FFT).

A vibration analysis method based on hybrid techniques and its application to rotating machinery

Vibration-based condition monitoring and fault diagnosis technique is a most effective approach to maintain the safe and reliable operation of rotating machinery. Unfortunately, the vibration signal always exhibits non-linear and non-stationary characteristics, which makes vibration signal analysis and fault feature extraction very difficult. To extract the significant fault features, a vibration analysis method based on hybrid techniques is proposed in this paper. Firstly, the raw signals are decomposed into a few product functions (PFs) using local mean decomposition (LMD), and meanwhile instantaneous frequency and instantaneous amplitude also are obtained. Subsequently, Fourier transform is performed on the derived PFs, and then, according to the spectra features, the useful PFs are selected to reconstruct the purified vibration signals. Lastly, several different fault features are fused to illustrate the operating state of the machinery. The experimental results show that the proposed method can accurately extract machine fault features, which proves that the combined application of LMD and other signal processing techniques is a successful scheme for the machine vibration analysis.

Detecting of transient vibration signatures using an improved fast spatial–spectral ensemble kurtosis kurtogram and its applications to mechanical signature analysis of short duration data from rotating machinery

Detecting transient vibration signatures is of vital importance for vibration-based condition monitoring and fault detection of the rotating machinery. However, raw mechanical signals collected by vibration sensors are generally mixtures of physical vibrations of the multiple mechanical components installed in the examined machinery. Fault-generated incipient vibration signatures masked by interfering contents are difficult to be identified. The fast kurtogram (FK) is a concise and smart gadget for characterizing these vibration features. The multi-rate filter-bank (MRFB) and the spectral kurtosis (SK) indicator of the FK are less powerful when strong interfering vibration contents exist, especially when the FK are applied to vibration signals of short duration. It is encountered that the impulsive interfering contents not authentically induced by mechanical faults complicate the optimal analyzing process and lead to incorrect choosing of the optimal analysis subband, therefore the original FK may leave out the essential fault signatures. To enhance the analyzing performance of FK for industrial applications, an improved version of fast kurtogram, named as “fast spatial–spectral ensemble kurtosis kurtogram”, is presented. In the proposed technique, discrete quasi-analytic wavelet tight frame (QAWTF) expansion methods are incorporated as the detection filters. The QAWTF, constructed based on dual tree complex wavelet transform, possesses better vibration transient signature extracting ability and enhanced time-frequency localizability compared with conventional wavelet packet transforms (WPTs). Moreover, in the constructed QAWTF, a non-dyadic ensemble wavelet subband generating strategy is put forward to produce extra wavelet subbands that are capable of identifying fault features located in transition-band of WPT. On the other hand, an enhanced signal impulsiveness evaluating indicator, named “spatial–spectral ensemble kurtosis” (SSEK), is put forward and utilized as the quantitative measure to select optimal analyzing parameters. The SSEK indicator is robuster in evaluating the impulsiveness intensity of vibration signals due to its better suppressing ability of Gaussian noise, harmonics and sporadic impulsive shocks. Numerical validations, an experimental test and two engineering applications were used to verify the effectiveness of the proposed technique. The analyzing results of the numerical validations, experimental tests and engineering applications demonstrate that the proposed technique possesses robuster transient vibration content detecting performance in comparison with the original FK and the WPT-based FK method, especially when they are applied to the processing of vibration signals of relative limited duration.