Vibration Analysis Techniques Research Articles

A Comparison of Hilbert and Fast Fourier Transform Techniques for Reciprocating Motors

Human existence has been significantly impacted by the growth of information and technology, particularly in relation to the requirement for effective mobility. But as a car ages, symptoms that affect its performance and safety start to show. These symptoms are a serious worry. Interference with car parts, such coils, is one issue that can have a major effect on driving safety and performance. Vibration analysis techniques have been successful in addressing this problem by detecting damage to different car parts. With the intention of comprehending piston motor vibration characteristics at different rotational speeds. The piston motor was used to collect vibration data at three distinct speeds: 900 rpm, 1500 rpm, and 3500 rpm. Both the Hilbert approach, which offers profound insight into frequency energy, and an FFT analyzer, which produces clear frequency spectrum information, were used in the analysis. The findings demonstrated that vibration characteristics changed in response to variations in reciprocating motor speed. On the other hand, the average frequency energy tends to remain constant across a range of speeds, suggesting that the features of reciprocating motors may be complicated and not very affected by variations in speed. According to this study, different perspectives on the vibration characteristics of piston motors operating at different speeds can be gained by utilizing vibration analysis techniques like FFT and Hilbert. Improved comprehension of this phenomenon could have a significant impact on maintenance initiatives and performance analysis of vehicles. To fully understand the complexity of vibration characteristics in reciprocating motors at various speed conditions, more research will be needed

Incorporating the Time-synchronous Averaging Method into Vibration Analysis Methodologies for the Detection and Localization of Bearing Defects

Objective: The objectives of this paper are to highlight the significance of vibration analysis, especially in predictive maintenance for rotating machinery, and to emphasize the importance of detecting bearing defects that may result in machinery failure. Methods: The proposed methodology combines the use of time-synchronous averaging (TSA) with existing vibration analysis techniques. TSA involves aligning vibration data with specific events or phases in the machinery's operation, such as shaft rotation. By synchronizing the data in this way, the methodology aims to reduce noise and enhance the signal related to bearing defects, making them more distinguishable. Additionally, the methodology incorporates well-established vibration analysis techniques. These techniques may include frequency analysis, amplitude modulation analysis, waveform analysis, and others commonly used in the field of condition monitoring and predictive maintenance. Results: The results of the analysis begin with waveform analysis, which involves examining the shape and pattern of vibration signals captured from the pinion. This analysis provides valuable insights into the dynamic behavior of the pinion gear, including any variations or abnormalities in its motion. Moreover, the use of synchronized waveforms is crucial in this analysis. By aligning the vibration data with specific events or phases in the gear mesh cycle, such as tooth engagement, the analysis can pinpoint moments when potential faults or wear in the machinery may occur. This synchronization allows for a more precise assessment of the vibration signals, enabling the detection of irregularities that may indicate underlying issues with the pinion or other components of the machinery. Conclusion: A pivotal aspect of the methodology involves envelope spectra analysis, significantly enhancing diagnostic capabilities. This analysis identifies fault patterns that might not be readily apparent in conventional vibration analysis. The incorporation of envelope spectra proves instrumental in proactive maintenance, enabling early detection of potential issues. This, in turn, contributes to the overall reliability and optimization of machinery performance.

Vibration Diagnostics for Predicting Pump Failures

This study investigates the predictive potential of specific vibration patterns in assessing the structural integrity of centrifugal pumps, vital for industrial fluid transport. Despite their importance, these pumps often encounter performance issues attributed to vibration-related damage such as cavitation and corrosion. Addressing this gap, we employ advanced vibration analysis techniques to identify distinct signatures indicative of cavitation and corrosion progression. Our research aims to provide an early detection tool for maintenance by understanding these patterns. Through our method, we reveal significant correlations between vibration patterns and pump condition, offering implications for extending pump lifespan and enhancing maintenance strategies in industries reliant on such machinery. Highlights: Centrifugal Pump Operation: Impeller rotation transfers fluid. Vibration Analysis Significance: Essential for predictive maintenance. Cavitation and Corrosion Effects: Trigger vibration, erode surfaces. Keywords: Centrifugal Pump, Corrosion, Vibration

Investigating Behavior of Slider–Crank Mechanisms with Bearing Failures Using Vibration Analysis Techniques

This study focuses on investigating the behavior of slider–crank mechanisms with different bearing failures using a vibration analysis technique. The reliability and lifespan of bearings are crucial for such mechanisms, which convert rotary motion to reciprocating motion. Previous research primarily addressed ball-bearing failures, neglecting needle bearings due to their specific applications. To bridge this gap, our experimental setup integrated both roller and ball bearings within a slider–crank mechanism. Vibration data were collected during normal operation, as well as under failure conditions of the ball and roller bearings. By analyzing the vibration signatures during simultaneous multiple failures, we gained insights into the nature of vibrations in the system. Furthermore, a mathematical model based on Hertzian contact was employed to calculate the theoretical frequency of ball bearings; however, due to the variable motion of the needle bearing, a novel mathematical model was proposed to estimate the defective impulse frequency, considering the inter-impact time between two impacts. The experimental results were compared with the healthy crank mechanism setup to draw meaningful conclusions. This research contributes to a comprehensive understanding of bearing failures in slider–crank mechanisms and provides valuable insights for designing reliable and long-lasting systems.

Diagnosis and root cause analysis of bearing failure using vibration analysis techniques

This paper is presented to diagnose and root cause analysis of the bearing failure. For this purpose, a vibration investigation was carried out on a motor with a 2300 kW power, 1489 rpm speed. Bearing failure was diagnosed by trending and analyzing vibration parameters. By investigating the bearing damage morphologies, a false brinelling pattern was observed. To find the failure cause, the machine was evaluated for two aspects; vibrations and lubrication. The resonance in the fan and the lack of full lubrication film formation were considered root causes of failure. To mitigate resonance, changing the operating speed was considered as a corrective action. By increasing the operating speed (1491 to 1530), the vibration amplitude decreased by nearly 21%. Also, the thickness of the lubricating film was improved by installing an airflow hose. Using this method, the time between failures increased from 3 to 11 months.

Diagnosis of localized defects in floating bush bearings through time-frequency domain analysis

Bearings play a crucial role in the functionality of rotating machinery, and any defects in these components can result in machine failure. Detecting, diagnosing, and prognosing bearing faults are crucial steps in machine failure diagnostics to prevent malfunctions and breakdowns. While various methods exist for fault detection, including acoustic emission analysis, visual inspection, thermography, ultrasonic, motor current analysis, wear-debris analysis, oil analysis, and vibration analysis, the latter stands out as a popular non-destructive method. This paper focuses on time and frequency domain vibration analysis techniques for detecting faults in floating bush bearings. Particularly beneficial for online monitoring, remote and non-human intervention areas, and hazardous locations, the time-frequency domain approach enhances diagnostic capabilities. The vibration data collected during these experiments has been rigorously analyzed using data acquisition system and applied a comprehensive approach that includes evaluating the data in both the time and frequency domains, as well as utilizing advanced signal processing techniques, notably the high-frequency resonance technique. Test results underscore the effectiveness of specific parameters in identifying defects: waveform, form factor, parameter K, and cepstrum excel in pinpointing external defects, while kurtosis, crest factor, and skewness prove adept at identifying internal faults. In the frequency domain, the enveloped spectrum emerges as a robust method for comprehensive defect detection. The vibration data presented in this paper will prove to be an invaluable resource for professionals engaged in the field of vibration measurement and analysis.

Monitoring of ball bearings via vibration analysis and envelope technique for predictive maintenance purposes

Bearings are one of the primary and most crucial machine components in the industry and transportation systems, since they are critical components in these systems, their failure can lead to various losses and significant damages. Despite the advanced stages of development and manufacturing processes, approximately 90% of unplanned machine shutdowns are attributed to bearing failures. These failures can occur in various ways, making it essential to monitor the bearing's deterioration to replace it before it reaches a critical condition for the industry, machine or vehicle's operation. To monitor ball bearing failures, a range of vibration monitoring techniques are employed, encompassing envelope analysis, kurtosis, bi-spectrum, and wavelet analysis. In this case, frequency graphs based in the signals of the bearings inner race will be generated, through a new signal acquisition system, using of envelope analysis by frequency filters, Hilbert Transform and Fast Fourier Transform (FFT). However, detecting and analyzing these signals can be challenging due to weak signals and noise masking vibration patterns. Although current techniques are effective, they have limitations, such as requiring expert analysis, difficulty in detecting early-stage faults, and inability to differentiate between different fault types. Current techniques, such as envelope and wavelet analysis, are effective but have limitations. New technologies and methods, are being explored to improve fault detection and classification, providing early detection of faults and differentiation between different fault types, ultimately reducing the impact of ball bearing failures on machines and industries. This paper proposed to present a study of the ball bearing failure through vibration analysis from early-stage to advanced-stage of damage for predictive maintenance purposes, applying the envelope and FFT together with programming, to enable the identification of defects in the bearing, especially in the inner race, through a signal acquisition system that can explain the presence of the defect through frequency graphs. Thus, obtaining results that show the presence of defects in three different bearings, with gradual defect magnitudes, differentiating these data from an ideal bearing. The next step we will intend to explore the new technologies like machine learning and artificial intelligence, to also analyze all variants of defects in a bearing.

Fault Detection Of 3 Phase Induction Motor Using Vibration Analysis

The induction motor is a critical component extensively employed in industrial applications. It is susceptible to various fault conditions during operation. These faults, which can arise due to multiple external factors, include overloading, single-phasing, unbalanced supply voltage, locked rotor, phase reversal, ground fault, under voltage, and overvoltage. To prevent damage and ensure worker safety, it is essential to promptly shut down the machine upon detecting a fault. Numerous types of faults can occur in induction motors, and identifying them often necessitates the utilization of vibration analysis techniques. These techniques require advanced signal processing methods to achieve rapid and dependable fault detection. Leveraging LabVIEW, a versatile and powerful software system for measurement and automation, can facilitate the creation of graphical interfaces for vibration detection.

Spectrum Prediction Indicating Bearing State in Induction Motor by Forced Vibration Analysis and Fuzzy Logic Technique

Spectrum Prediction Indicating Bearing State in Induction Motor by Forced Vibration Analysis and Fuzzy Logic Technique

Automatic detection of bearing faults

The potential for machine Condition Monitoring (CM) to enhance system performance and forestall harmful failures has been rising considerably. It is applicable to all or any rotating machinery where preventive fault diagnosis could be a must like Engines, drive trains, gearboxes, pumps, turbines, compressors, fans etc. Detection of bearing faults is one in all the foremost challenging tasks in bearing health condition monitoring, especially when the fault is at its initial stage. The defects in bearing unless detected in time may result in malfunctioning of the machinery. Acquisition of vibration signals coming from various machine components, performing its analysis for prediction of faults and understanding the root cause for high vibrations has been an established practice. However, while performing analysis of vibration signals, it is close to impossible to separate out and focus on ‘bearing frequencies’ especially in the presence of strong masking signals from other machine components. In this paper a study and implementation of ‘advanced’ vibration analysis technique viz envelope analysis for localization of bearing frequencies from the masking signals generated by other machine components is discussed. A smart graphical user interface-based software tool has been developed for automatic detection of bearing faults. The automatic detection techniques presented in this paper can be very helpful for condition monitoring especially for early diagnosis of bearing faults. This may help industries for minimizing the downtime due to machine breakdown. With the help of vibration sensor (accelerometer), FFT analyzer, set of faulty bearings installed with rotating arrangement and a graphical user interface-based software tool designed with the help of multi-paradigm programming language, the setup for automatic detection of bearing faults has been validated.

Detection of unbalance defect by the vibration analysis technique

Abstract The main advantage of condition monitoring by analyzing vibrations on rotating machines is the possibility of detecting faults before a failure occurs leading to the unplanned stoppage of a machine. The unbalance defect is the most common and most frequently encountered cause of vibration, it is also one of the main causes of reduction in the life of machines and it constitutes one of the main risks for Security. This paper is devoted to the experimental study of the two types of unbalance defects, their different vibratory signatures and how to differentiate their vibratory behavior from other mechanical faults which manifests itself by the presence of a peak at the rotational frequency in the case of combined faults.

Vibration Analysis and Digital Image Correlation Techniques for a Suspension Bridge

Bridge inspection is critical to safe transportation, especially in highly urbanized areas. Frequent inspection invoked by the un-scheduled maintenance for bridges is often subjected to the availability of NDT service providers. Hence the objective of the current research is to provide an economical NDT technique for the maintenance crew. Digital image correlation techniques can be cost-effective for the displacement measurement of highway bridges using merely one high-resolution camera. The vibration analysis based on the remote measurements of high-speed camera systems is not only challenging but also prohibitively expensive. An alternative approach using two consumer-grade digital cameras has been developed. The field test has been successfully carried out for a 50-meter-span suspension bridge. Satisfactory results have been obtained in both the vertical displacement caused by a moving load and the mode shapes of the first and second vibration modes.

Intelligent Vibration Analysis of Industrial Cooling Fans

Industrial cooling fans are responsible for maintaining stable temperatures for delicate components. Therefore, a cooling system failure can certainly lead to machine downtime. Fault Condition Monitoring (FCM) is a predictive maintenance method that can be applied to cooling fans for fault prediction. As the components of a cooling fan wear off, its vibration tends to vary. Thus, this paper uniquely elaborates on three intelligent vibration analysis techniques that are applicable in the FCM of cooling fans. In this research, 1) image encoding with convolutional neural network (CNN), 2) moving average, and 3) fuzzy logic techniques are designed, employed, and their potentials as FCM tools are compared. The vibration data is collected from an experimental test bench that consists of a fan, an accelerometer, and a microcontroller, among others. Once enough data is obtained, the three vibration analysis techniques are applied using Python and MATLAB. The results reported in this paper demonstrate the effectiveness of these intelligent vibration analysis techniques in the FCM of cooling fans and possibly other industrial equipment. The novelty of the research revolves around the fan fault classification techniques that are being compared. The image-encoding technique described in this paper has yet to be applied for fault classification. Additionally, while fuzzy logic and moving average are popular methods, this is the first time that they are being used for vibration analysis of cooling fans. Furthermore, this is also a novel comparative study of different vibration analysis techniques.

A novel similarity-based status characterization methodology for gear surface wear propagation monitoring

The gearbox is a vital component for rotating machinery and has been used in many critical engineering applications. Surface wear is a common but inevitable phenomenon during the lifespan of the gearbox. Its propagation can result in some catastrophic failures and cause unexpected economic loss. Therefore, it is vital to evaluate the degradation process of the gear system caused by surface wear propagation in order to make reliable predictive maintenance-based decisions to ensure the safe operation of the gearbox transmission system. The vibration analysis technique is a prevailing tool for rotating machine condition monitoring. However, research on vibration-based gear wear monitoring is relatively rare as the dynamic interactions between gear surface wear and gear system dynamic characteristics would produce complex gear dynamic responses and vibration features. Therefore, this paper presents a novel similarity-based status characterization methodology for gear wear monitoring. In this proposed methodology, a novel gear wear monitoring indicator is developed to evaluate gear tooth contact characteristics at different wear severities, which could significantly benefit gear systems' health management. The effectiveness of the proposed method for gear wear propagation process monitoring is presented and proven through a series of run-to-failure tests with different lubrications and operational conditions.

Recent advancements of signal processing and artificial intelligence in the fault detection of rolling element bearings: a review

A rolling element bearing is a common component in household and industrial machines. Even a minor fault in this section has a negative impact on the machinery's overall operation. As a result, the industry suffers significant financial losses, and this damage can potentially result in catastrophic failures. Therefore, even a little fault in the rolling element bearings must be recognized and remedied as soon as possible. Many ways for detecting REB defects have been created in recent years, and new methods are being introduced on a daily basis. This article will provide a summary of such methods, with a focus on vibration analysis techniques. The newest advancements in this field will be recognizable to readers of this article. Anyone interested in defect diagnostics of rolling element bearings can utilize this material.

Chebyshev-Lagrange Multipliers Technique for Vibration Analysis of Functionally Graded Material Beams Using Various Beam Theories

Chebyshev-Lagrange Multipliers Technique for Vibration Analysis of Functionally Graded Material Beams Using Various Beam Theories

Fault diagnosis method of rotating bearing based on improved ensemble empirical mode decomposition and deep belief network

Based on the analysis of the bearing rotating speed feature and the vibration analysis technique, a novel fault diagnosis method of rotating bearing by adopting improved ensemble empirical mode decomposition (EEMD) and deep belief network (DBN) was proposed. Firstly, the EEMD method is adopted to decompose the collected vibration data into the combination of the several intrinsic mode functions (IMFs). Then the spectrum of IMF components and the spectrum of original data are compared to eliminate the false components and interference signals. Because the redundant extreme points or pseudo-extreme points seriously affect the construction of the mean curve of EEMD algorithm, three methods are put forward to eliminate the pseudo extreme points and the redundancy extreme points so as to improve the algorithm performance. Finally, the remaining IMF components are entered into the DBN to extract the data features and realize the fault diagnosis. Simulation results on the rolling bearing data of the Bearing Data Center in Case Western Reserve University show the effective of the proposed method.

Vibration characteristics of submerged floating tunnels with suspension cables according to wave periods

The tension leg method is typically used for the mooring of submerged floating tunnels (SFTs). These tunnels may have mooring lines and foundations installed very close together, depending on the wave conditions. Sometimes, the construction cost can be increased by the foundation design. To overcome these weaknesses, a suspension SFT using the mechanism of a suspension bridge has been proposed. In this approach, the tunnel body is connected to fixed towers at both ends. Two main cables are installed for controlling the tunnel motion. The main cables are connected to the tunnel using hangers. The main cable is a steel pipe and filled with water to increase its weight. The main cables are arranged in an arch shape for controlling the buoyancy weight ratio (BWR) of the tunnel body. The present study analyzed the vibration characteristics of a suspension SFT using the free vibration analysis and time-domain analysis techniques. The study demonstrates that the SFT has excellent self-vibration control. The suspension SFT affected tuned mass damping via the interaction between the tunnel body and main cables.

Experimental Investigation of Vibration Analysis on Implant Stability for a Novel Implant Design

Osseointegrated prostheses are widely used following transfemoral amputation. However, this technique requires sufficient implant stability before and during the rehabilitation period to mitigate the risk of implant breakage and loosening. Hence, reliable assessment methods for the osseointegration process are essential to ensure initial and long–term implant stability. This paper researches the feasibility of a vibration analysis technique for the osseointegration (OI) process by investigating the change in the dynamic response of the residual femur with a novel implant design during a simulated OI process. The paper also proposes a concept of an energy index (the E–index), which is formulated based on the normalized magnitude. To illustrate the potential of the E–index, this paper reports on changes in the vibrational behaviors of a 133 mm long amputated artificial femur model and implant system, with epoxy adhesives applied at the interface to simulate the OI process. The results show a significant variation in the magnitude of the colormap against curing time. The study also shows that the E–index was sensitive to the interface stiffness change, especially during the early curing process. These findings highlight the feasibility of using the vibration analysis technique and the E–index to quantitatively monitor the osseointegration process for future improvement on the efficiency of human health monitoring and patient rehabilitation.

Axial vibration of deep-water drilling risers under lifting conditions

During the installation of deep-water drilling risers, the axial vibration caused by the heaving of drilling ships affects the safety and reliability of the operation. Conventional vibration analysis techniques consider the damping generated by the drag force to be uniformly distributed along with the risers, resulting in not real solutions. In this paper, an axial dynamics model of the drilling risers under the top excitation conditions is established, where the riser system is regarded as the assembly of an elastic rod and a block mass. Meanwhile, this model replaces the uniform damping with nonlinear distributed damping and considers the nonlinear concentrated damping of the block mass, which can be solved by the variable separation method and the iterative method. Consequently, the stress and displacement in the riser given by the present model are larger than that predicted by the model considering uniform damping or no damping. In addition, the sensitivity analysis of some parameters like the damping coefficient, the excitation amplitude and frequency, the lifting water depth, and the quality of the block mass was carried out. It shows that the stress in the top is the largest when the riser is lowered regardless of its self-weight, and it decreases almost linearly with the increase of water depth; the axial displacement increases with the increase of the block mass and the lowering water depth but reduces with the increase of the dimensionless damping coefficient. Moreover, excessive excitation frequency will increase the equivalent damping, resulting in greater stress.