Vibration-based Condition Monitoring Research Articles

Review of Data Processing Methods Used in Predictive Maintenance for Next Generation Heavy Machinery

Vibration-based condition monitoring plays an important role in maintaining reliable and effective heavy machinery in various sectors. Heavy machinery involves major investments and is frequently subjected to extreme operating conditions. Therefore, prompt fault identification and preventive maintenance are important for reducing costly breakdowns and maintaining operational safety. In this review, we look at different methods of vibration data processing in the context of vibration-based condition monitoring for heavy machinery. We divided primary approaches related to vibration data processing into three categories–signal processing methods, preprocessing-based techniques and artificial intelligence-based methods. We highlight the importance of these methods in improving the reliability and effectiveness of heavy machinery condition monitoring systems, highlighting the importance of precise and automated fault detection systems. To improve machinery performance and operational efficiency, this review aims to provide information on current developments and future directions in vibration-based condition monitoring by addressing issues like imbalanced data and integrating cutting-edge techniques like anomaly detection algorithms.

Benefits of Mann–Kendall trend analysis for vibration-based condition monitoring

Rank-based statistics offer an appealing framework to non-parametric robust trend detection. Especially, the Mann–Kendall test detects the presence of a trend in a series. This paper investigates the benefits of using the Mann–Kendall test, which seems to have remained largely unnoticed in the context of vibration-based condition monitoring. Two contributions structure the present paper. First, theoretical foundations of rank-statistics are reminded and the Z-score is introduced, a statistical metric measuring the presence of a trend in a given series. The performance of the Mann–Kendall test is investigated and confronted to different numerical cases involving series of increasing condition indicators. Second, based on this understanding, the Z-score metric is exploited to design two trend-oriented signal processing tools dedicated to vibration-based condition monitoring. Namely, a new tool named the Mannagram is introduced as a dyadic filterbank representation of the trend of indicators series. It shows effective in the informative band selection problem and unveils more interpretable indicators. Besides, a concise representation of trend by frequency bin is introduced and coined the Kendrum. This representation is shown to help the diagnosis by summarising the trend information of series of spectra. Both methods are demonstrated on run-to-failure series of vibration signals from industrial wind turbines.

Effectiveness of Vibration-Based Techniques for Damage Localization and Lifetime Prediction in Structural Health Monitoring of Bridges: A Comprehensive Review

Bridges are essential to infrastructure and transportation networks, but face challenges from heavier traffic, higher speeds, and modifications like busway integration, leading to potential overloading and costly maintenance. Structural Health Monitoring (SHM) plays a crucial role in assessing bridge conditions and predicting failures to maintain structural integrity. Vibration-based condition monitoring employs non-destructive, in situ sensing and analysis of system dynamics across time, frequency, or modal domains. This method detects changes indicative of damage or deterioration, offering a proactive approach to maintenance in civil engineering. Such monitoring systems hold promise for optimizing the management and upkeep of modern infrastructure, potentially reducing operational costs. This paper aims to assist newcomers, practitioners, and researchers in navigating various methodologies for damage identification using sensor data from real structures. It offers a comprehensive review of prevalent anomaly detection approaches, spanning from traditional techniques to cutting-edge methods. Additionally, it addresses challenges inherent in Vibration-Based Damage (VBD) SHM applications, including establishing damage thresholds, corrosion detection, and sensor drift.

Enhancing Wireless Sensor Network in Structural Health Monitoring through TCP/IP Socket Programming-Based Mimic Broadcasting: Experimental Validation

This paper presents the implementation of a synchronous Structural Health Monitoring (SHM) framework utilizing wireless, low-cost, and off-the-shelf components. Vibration-based condition monitoring plays a crucial role in assessing the reliability of structural systems by detecting damage through changes in vibration parameters. The adoption of low-cost Micro-Electro-Mechanical Systems (MEMS) sensors in Wireless Sensor Networks (WSNs) has gained traction, emphasizing the need for precise time synchronization to schedule wake-up times of multiple sensor nodes for data collection. To address this challenge, our proposed method introduces a TCP/IP socket programming-based mimic broadcasting mechanism and a scalable sensing network controlled by a central gateway, leveraging the Raspberry Pi Python platform. The system operates using Internet of Things (IoT) concepts and adopts a star topology, where a packet is transmitted from the gateway to initiate measurements simultaneously on multiple sensor nodes. The sensor node comprises a MEMS accelerometer, a real time clock DS3231 module and Raspberry Pi Zero 2W (RPi0-2W), while the gateway employs a Raspberry Pi 4 (RPi4). To ensure accurate time synchronization, all Pi0-2W nodes were configured as Network Time Protocol (NTP) clients, synchronizing with an RPi4 server using chrony, the reliable implementation of the NTP. Through experimental evaluations, the system demonstrates its effectiveness and reliability in achieving initial time synchronization. This study addresses the challenge of achieving precise time alignment between sensor nodes through the utilization of the Dynamic Time Wrapping (DTW) method for Frequency Domain Decomposition (FDD) applications. The contribution of this research significantly enhances the field by improving the accuracy and reliability of time-aligned measurements, with a specific focus on utilizing low-cost sensors. By developing a practical and cost-effective SHM framework, this work advances the accessibility and scalability of structural health monitoring solutions, facilitating more widespread adoption and implementation in various engineering applications

A novel dust collection system fault diagnosis based on the noise emission of solenoid pulse valve

The solenoid pulse valve (SPV) is a key component of dust collector. The frequent switch of SPV accelerates its damage, subsequently decrease operation efficiency of dust collector. The traditional manual inspection methods lack reliability and efficiency, while vibration-based condition monitoring is costly and sophisticated in acceleration sensor distribution and signal processing. This paper proposes a fault diagnosis approach based on SPV noise. The average coherence coefficient of SPV noise and vibration signals is above 0.8 and close to 1, which demonstrates that there is a close relationship between SPV noise and its structural vibration. Furthermore, the validation experiments indicate that the SPV fault can be clearly distinguished by its noise signal, confirms the effectiveness of proposed approach. By utilizing this noise monitoring method, the number of sensors can be reduced by about 95 %, the work intensity of the staff is greatly reduced, and production efficiency and safety are guaranteed.

Low cost MEMS accelerometer and microphone based condition monitoring sensor, with LoRa and Bluetooth Low Energy radio

Vibration-based Condition Monitoring (CM) is an essential tool for identifying potential defects in industrial machinery. However, the implementation of an efficient CM system often necessitates the use of high-cost accelerometers with a large bandwidth. To address this challenge, this study introduces a low-cost CM sensor composed of an ultrasonic MEMS microphone - SPH0641LU and an ADXL1002 MEMS accelerometer. The combination of these two sensor types allows for comparative analysis of the captured data. The SPH641LU microphone is capable of detecting audible vibration signals with a frequency range up to 80 kHz, while the ADXL1002 accelerometer can measure vibrations up to 21 kHz. In addition a three axis ultra low power accelerometer is included, allowing measurement of unbalance or rotating speed, below 200Hz. Moreover, the sensor is designed to operate on battery power and provides the capability for raw data transmission via Bluetooth Low Energy (BLE) or the transmission of pre-processed features from the raw data using LoRaWAN.

An In-Depth Study of Vibration Sensors for Condition Monitoring

Heavy machinery allows for the efficient, precise, and safe management of large-scale operations that are beyond the abilities of humans. Heavy machinery breakdowns or failures lead to unexpected downtime, increasing maintenance costs, project delays, and leading to a negative impact on personnel safety. Predictive maintenance is a maintenance strategy that predicts possible breakdowns of equipment using data analysis, pattern recognition, and machine learning. In this paper, vibration-based condition monitoring studies are reviewed with a focus on the devices and methods used for data collection. For measuring vibrations, different accelerometers and their technologies were investigated and evaluated within data collection contexts. The studies collected information from a wide range of sources in the heavy machinery. Throughout our review, we came across some studies using simulations or existing datasets. We concluded in this review that due to the complexity of the situation, we need to use more advanced accelerometers that can measure vibration.

On the effectiveness of standards application to threshold setting in vibration condition monitoring in industrial machinery

One of the goals of Industry 4.0 is to minimize unplanned maintenance emergencies and to leverage the efficiency of condition-based, just-in-time labor and repairs. Achieving the benefits of predictive maintenance via an Industry 4.0-style plant requires not only a data capture ecosystem, but also reference values to inform the monitoring system. Effectively estimating the values of monitoring thresholds is not an easy task, especially in those cases where the manufacturer does not provide data, or the machine is not new but the monitoring system has just been installed, therefore a measurement history is not yet available. The purpose of the present work is assess, through the analysis of two separate test cases, whether the monitoring thresholds suggested by different internationally recognized standards may serve as effective starting values in a vibration-based condition monitoring system. While general agreement on the order of magnitude of the thresholds is present in all cases, the differences between the standards allow for conclusions to be drawn on the most convenient format and processing technique to handle the data and to assess the most/least conservative standards. This paper constitutes, to the authors knowledge, the only systematic study in which the recommendations of all available international standards for vibration monitoring of non-rotating parts are applied to the same test cases and compared.

Time-Inferred Autoencoder: A noise adaptive condition monitoring tool

Vibration-based condition monitoring relies on capturing spatio-temporal characteristics (STCs) such as resonant frequencies and operational deflection shapes to assess the health of a system. Optical techniques have gained popularity in this field, but noise in the optical data can affect the accuracy of extracted STCs. To address this issue, ad-hoc denoising methods like total variation denoising (TVD) and deep learning-based algorithms have been used. However, these methods are often specific to particular applications. This study proposes a robust time-inferred autoencoder (TIA) framework to preserve a system’s STCs while denoising its optically collected response. The TIA model is trained using videos of an undamaged vibrating structure to learn its underlying STCs. It is then used to reconstruct the dynamic response of damaged configurations of the same structure. The performance of TIA in reconstructing the dynamic response and denoising is compared to CNN-based autoencoders and TVD. Laboratory tests were conducted, and the results showed that TIA achieved an accuracy of approximately 94% in extracting the STCs, outperforming CNN-based autoencoders by around 40%. At the same time, TIA demonstrated comparable denoising accuracy as TVD. However, TIA offers more flexibility and automated processes over TVD, resulting in a case-independent method. Once the TIA model is trained, it does not require manually selecting or updating the regularizer term if the input dataset changes. Further development of the TIA framework could enhance its capabilities and enable its broader application as a robust tool for condition monitoring, contributing to improved system health assessment.

A Mechanical-Electromagnetic Coupling Model of Transformer Windings and its Application in the Vibration-Based Condition Monitoring

Winding vibration leads to the oscillation of coils position and further affects winding's nonlinear stiffness as well as electromagnetic forces (EMFs). This paper establishes a winding Mechanical-Electromagnetic Coupling Model (MECM) where the coupling between stiffness coefficient and vibration, magnetic field and vibration are considered, and deploy it in the vibration-based condition monitoring. In this model, EMFs are obtained through the superposition of leakage magnetic field generated by each coil. A novel numerical analysis method is further proposed, which is capable of calculating the vibration response containing nonlinearity and electromechanical coupling, and the accuracy of the method is verified in experiments. Then, the numerical analysis method is used to quantitatively analyze the effects of Nonlinearity and ElectroMechanical Coupling (NEMC) on the vibration characteristics. The study demonstrates the law that NEMC intensity increases with vibration, and the NEMC is obvious especially under mechanical fault. To extract the vibration patterns, Nonlinearity-ElectroMechanical Coupling Coefficient (NEMCC) with amplifying NEMC effects is proposed to monitor the winding mechanical condition. The analytical method predicts the variation trend of the winding looseness condition curve, and laboratory experiments prove that the Mean Absolute Percentage Error (MAPE) is able to successfully identify different degrees of winding looseness faults. Compared with the short-circuit impedance (SCI) method, the MAPE value based on NEMCC has a stronger detection capability and higher sensitivity for winding mechanical faults. This paper lays a foundation for the numerical solution of vibration response and pave a way for winding mechanical condition monitoring.

Latent indicators for temporal-preserving latent variable models in vibration-based condition monitoring under non-stationary conditions

Condition-based monitoring for critical assets is reliant on the quality of the indicators used for condition inference. These indicators must be sensitive to the development of faults under constant and non-stationary operating conditions. Latent variable models in the time-preserving framework offer a powerful learning-based technique for the monitoring of critical assets as they only require healthy asset data, and provide indicators from the data space for asset condition inference. However, the latent manifold of latent variable models is often disregarded in favour of data space indicators, such as reconstruction errors, which are primarily focused on measuring the likelihood of the observed data. The latent space is often unintentionally unutilised. In this work, we highlight that the latent manifold is a powerful resource for condition inference and should be utilised for condition monitoring. We conceptually categorise and identify five classes of latent space health indicators that capture various manifold perspectives. These five classes introduce and allow for latent health indicator derivation and are useful for the condition inference task. Fifteen latent health indicators are considered in this work and are applied to the fault diagnostics task on two experimental datasets. An ensemble-based inference procedure is used, which produces a modular fault diagnosis framework. The indicators are shown to be informative for condition inference in both constant and variable operating conditions. Utilising the latent manifold in condition monitoring tasks is important to further develop the learning-based condition monitoring field.

Nonlinear dynamic analysis on centrifugal stiffening of rotating cracked tapered functionally graded beam for flap-wise vibrations

Vibration-based condition monitoring for remotely located rotating blades is one of the important aspects to avoid sudden breakdown of the machinery. Vibration analysis of damaged blades provides the information to identify the damage during operation. In this work, dynamic attributes of rotating cracked tapered cantilever functionally graded material (FGM) beams for various tapered configurations are investigated using energy method. The nonlinearity due to in plane stretching as a result of large deformations is taken in to consideration. Nonlinear governing equations for cracked rotating tapered FGM beams are generated by optimizing the energy functional. An iterative algorithm is developed to solve the derived nonlinear equations. The effects corresponding to location of crack and centrifugal stiffening on the various modes has been presented for different tapered configurations, material indices, crack depths and rotational speeds. Crack compensating speeds are studied and reported for different tapered ratios and crack parameters for the first time. The effect of crack location and crack depth on the first two nonlinear frequency ratios is also investigated for different tapered configurations and material indices. The influence of centrifugal stiffening on nonlinear responses of FGM beams is presented for various crack parameters and tapered configurations.

이벤트 센서를 활용한 운전 중 장비 상태 감시 가능성 고찰

Vibration-based condition monitoring technology is regarded as one of the most effective maintenance technologies. Many studies aimed at developing a vision sensor-based condition monitoring system using image processing technologies are being conducted. Event sensors such as dynamic vision sensor have a different operating principle and have many advantages compared to the traditional frame-based sensors; however, for event sensor-based vibration measurement and analysis, the formalized methodology does not exist as an image processing technology. In this study, optical flow and template matching methods were used as tracking methods for event sensor-based condition monitoring. The vibration measurement performance of the representative methods was examined through experiments using a simulator with vertical and horizontal movements. Furthermore, the possibility of extracting changes in the vibration signals according to the state of simulator was investigated.

Transmission-error- and vibration-based condition monitoring of gear wear with contaminated lubricant

The presence of contaminant particles in lubricants could cause severe gear wear and damage. Conventional condition monitoring techniques, i.e., gear inspection and oil/wear debris analysis, are often utilised to assess the presence of wear due to contaminants and its severity such as wear depth. However, the traditional approaches require experience or off-line analysis. Although vibration is a non-invasive technique for fault detection and diagnostics, it is less well developed for wear analysis. Recently, an alternative sensor technology, i.e., transmission error (TE), combining angular measurements obtained from the input and output shafts has shown strong potential for wear monitoring. This work aims to investigate the usage of vibration and TE to identify the existence of contaminants and measure the gear wear severity caused by the oil contamination. This is achieved by conducting gear wear tests on a spur gearbox rig, using clean and contaminated lubricants with silica sand. The test in a normal lubrication condition provides a healthy reference of the spur gearbox under a nominal operating condition. In addition to the nominal operating condition, TE and vibration measurements are collected throughout the tests at different operating conditions, together with moulds of the gear tooth surfaces for wear analysis. The results show that the absolute TE can quantitively measure wear depth, and the RMS of vibration can qualitatively correlate with the trend of the average wear depths. The proposed technique extends the field of using TE to diagnose and predict gear wear in contaminated lubricant conditions.

The Influence of Gravitational Stiffening and De-stiffening on Nonlinear Dynamic Behavior of Vertical Beams with Cracks

ABSTRACT Dynamics of vertically oriented blades is an important topic of research as it replicates the physical behaviour of several real-time applications such as horizontal axis wind turbine blades. It is observed that available literatures explaining the effects of gravity on the dynamic behaviour of vertical beams/blades did not focus on the various operational adversities such as geometric nonlinearities, possibilities of crack initiation due to adverse conditions. Therefore, in present study, an attempt is made to capture reverberations of gravity on cracked vertical blades including geometric nonlinearity. A computationally efficient numerical model for nonlinear dynamic analysis of vertically oriented cracked blades is developed. Efficacy of proposed model is verified by several case studies. Subsequently, parametric investigation is performed for combined effects of crack and gravity on the dynamic response. Crack and gravity dominant regions based on slenderness ratio are identified for the first time . Furthermore, influence of nonlinearities on such behaviour provides useful insights to anticipate the mechanics of the dynamic behaviour of a vertical cracked cantilever beam. Proposed work may be useful for developing a robust vibration-based condition monitoring for a given application which involves hanging beams with a possibility of crack such as rotor blades.

Implementation of a Condition Monitoring Strategy for the Monastery of Salzedas, Portugal: Challenges and Optimisation

The implementation of condition monitoring for damage identification and the generation of a reliable digital twin are essential elements of preventive conservation. The application of this promising approach to Cultural Heritage (CH) sites is deemed truly beneficial, constituting a minimally invasive mitigation strategy and a cost-effective decision-making tool. In this light, the present work focuses on establishing an informative virtual model as a platform for the conservation of the monastery of Santa Maria de Salzedas, a CH building located in the north of Portugal. The platform is the first step towards the generation of the digital twin and is populated with existing documentation as well as new information collected within the scope of an inspection and diagnosis programme. At this stage, the virtual model encompasses the main cloister, whose structural condition and safety raised concerns in the past and required the implementation of urgent remedial measures. In the definition of a vibration-based condition monitoring strategy for the south wing of the cloister, five modes were identified by carrying out an extensive dynamic identification. Nonetheless, significant challenges emerged due to the low amplitude of the ambient-induced vibrations and the intrusiveness of the activities. To this end, a data-driven Optimal Sensor Placement (OSP) approach was followed, testing and comparing five heuristic methods to define a good trade-off between the number of sensors and the quality of the collected information. The results showed that these algorithms for OSP allow the selection of sensor locations with good signal strength.

An optical flow-based method for condition-based maintenance and operational safety in autonomous cleaning robots

Autonomous professional cleaning robots are ubiquitous today, coexist with humans, and the demand continues to improve hygiene and productivity, especially in large indoor workplaces. Hence, a proper maintenance strategy is essential to monitor the robot’s health, assuring flawless operation and safety. Manual supervision and periodic maintenance are adopted in general, which is challenging to detect failures in advance, resulting in higher maintenance costs and operational hazards. Anomalous vibration due to system degradation or environmental factors is an early symptom of failure or potential threats. Hence, predicting the sources of such abnormal vibration will help prompt maintenance action or set a hazard-free environment. However, such condition-monitoring research studies are not common for indoor cleaning robots. To fill this gap, we proposed an automated Condition Monitoring (CM) system, predicting the abnormal vibration sources that will enhance Condition-based Maintenance (CbM) and Operational Safety. A novel vibration-based CM method is presented for mobile robots, suitable for indoor environments under typical illumination states, using a monocular camera and optical flow technique instead of their usual perception-related applications. The sources of abnormal vibration were classified as terrain, collision, loose assembly, and structural imbalance. We modelled vibration as sparse optical flow, and the change in optical flow vector displacement due to the robot’s vibration is derived from consecutive camera frames used as vibration data. A practical framework is developed adopting the One Dimensional Convolutional Neural Network (1D CNN) model to fit this vibration data and tested predicting the vibration source class with an average accuracy of 93.8%. In addition, a vibration source map (CbM map) is proposed by fusing the predicted class in the workplace environment map for real-time monitoring. The case studies conducted using our in-house-developed cleaning robot show that the proposed CM framework will help the maintenance team for CbM, operational safety, and select proper maintenance strategies.

Towards real time automated early gear failure detection

The ability to stop a gear fatigue test before catastrophic failure has many advantages. However, today, a widely accepted approach is not available. This case study applies a vibration-based condition monitoring methodology to detect early gear failures. The gear studied takes part in an all-wheel-drive drivetrain system. Vibration signals from four run-to-failure fatigue tests at two constant torque-speed combinations were used as input to time-synchronous averaging and autoregression model generation. The applied methodology shows promising results for early failure detection, and the process is feasible for implementation in an automated environment. Real time analysis is also possible since the autoregression model generates a healthy state TSA signal during the early testing stages. However, the time to failure detection varies with operating conditions, with low sensitivity at high-speed and low-torque conditions.

Explainable AI Algorithms for Vibration Data-Based Fault Detection: Use Case-Adadpted Methods and Critical Evaluation

Analyzing vibration data using deep neural networks is an effective way to detect damages in rotating machinery at an early stage. However, the black-box approach of these methods often does not provide a satisfactory solution because the cause of classifications is not comprehensible to humans. Therefore, this work investigates the application of the explainable AI (XAI) algorithms to convolutional neural networks for vibration-based condition monitoring. Thus, the three XAI algorithms GradCAM, LRP and LIME with a modified perturbation strategy are applied to classifications based on the Fourier transform as well as the order analysis of the vibration signal. The following visualization as frequency-RPM maps and order-RPM maps allows for an effective assessment of saliency values for variable periodicity of the data, which translates to a varying rotation speed of a real-world machine. To compare the explanatory power of the XAI methods, investigations are first carried out with a synthetic data set with known class-specific characteristics. Both a visual and a quantitative analysis of the resulting saliency maps are presented. Then, a real-world data set for vibration-based imbalance classification on an electric motor, which runs at a broad range of rotation speeds, is used. The results indicate that the investigated algorithms are each partially successful in providing sample-specific saliency maps which highlight class-specific features and omit features which are not relevant for classification.

Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

Vibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effective method for predictive maintenance because it is used to troubleshoot instantaneous faults as well as periodic maintenance. Numerous studies conducted in this vein have been published in a variety of outlets. This review documents data-driven and recently published deep learning techniques for vibration-based condition monitoring. Numerous studies were obtained from two reputable indexing databases, Web of Science and Scopus. Following a thorough review, 59 studies were selected for synthesis. The selected studies are then systematically discussed to provide researchers with an in-depth view of deep learning-based fault diagnosis methods based on vibration signals. Additionally, a few remarks regarding future research directions are made, including graph-based neural networks, physics-informed ML, and a transformer convolutional network-based fault diagnosis method.