Condition Monitoring Of Machinery Research Articles

Antagonistic strategy for enhanced performance polyimide-based triboelectric nanogenerators and wireless self-powered sensors in extremely harsh environments

Triboelectric nanogenerators (TENGs) offer promising green energy solutions for portable microelectronics in the Internet of Everything era. However, their practical applications are limited by low electrical output and stability in harsh environments. This study introduces an innovative antagonistic strategy to enhance TENG performance and stability using thermo-stable polyimide with customizable porous structures (ip-PI). By employing antagonistic solubility, we create ip-PI using chloroform as a solvent for PI and acetone as a solvent for the Poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-co-HFP) template, whereas they both act as nonsolvents for their respective counterparts. Additionally, microbeads-patterned chitosan (mb-CS) is rationally designed and fabricated as morphologically and electrically antagonistic counter surfaces for ip-PI. The resulting antagonist-inspired TENG (AI-TENG) constructed from ip-PI and mb-CS achieves a remarkable average power density of 4 W·m−2, presenting an outstanding 200-fold increase in power compared to TENG made from unstructured flat surfaces. Furthermore, the ip-PI demonstrates excellent temperature resistance, enabling the TENG to operate at extremely high temperatures up to 250 °C, with promising durability of over 10,000 contact-separation cycles. Applications include self-powered machinery condition monitoring sensors and a wireless communication fire forest pre-warning system, showcasing significant advancements in flexible, high-performance TENGs suitable for industrial use in extreme environments.

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.

Optimizing bearing health condition monitoring: exploring correlation feature selection algorithm

Vibration signals are a critical source of information for detecting and diagnosing bearing faults, making this research particularly relevant to the condition monitoring of industrial machinery, particularly bearings using vibration signals. This study delves into how feature selection can be done using Pearson’s Correlation Co-efficient within the context of monitoring bearing health conditions, utilizing two distinct approaches. Approach-1 involves feature selection without considering labels, while Approach-2 incorporates labels for feature selection. Comparative analysis is conducted against outcomes obtained when all features are selected. The research scrutinizes the impact of feature selection on classifier performance, accuracy, and execution times, utilizing various machine learning algorithms such as Decision Tree (DT), K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB). The findings underscore that feature selection significantly enhances classifier accuracy while reducing execution times. Specifically, only DT and KNN with 50 neighbors achieved 100% accuracy when all features were considered. However, with feature selection using Approach-1 (without labels), DT, KNN, SVM (excluding 100 neighbors), and NB (with Normal/Gaussian kernel) attained 100% accuracy. Employing Approach-2 (with labeled features), DT with 0.7 and 0.9 thresholds, SVM-G with all thresholds (0.6, 0.7, and 0.9), KNN with all thresholds (except 100 neighbors), and NB-n (with all thresholds) achieved 100% accuracy. The study emphasizes the pivotal role of feature selection using Pearson’s Correlation Coefficient in enhancing machine learning classifier performance, offering promising avenues for future research and practical applications across diverse domains.

A Family of Health Indicators Induced by EOF for Condition Monitoring of Machinery

A Family of Health Indicators Induced by EOF for Condition Monitoring of Machinery

A Flexible, Adaptive, and Self-Powered Triboelectric Vibration Sensor with Conductive Sponge-Silicone for Machinery Condition Monitoring.

Vibration sensors for continuous and reliable condition monitoring of mechanical equipment, especially detection points of curved surfaces, remain a great challenge and are highly desired. Herein, a highly flexible and adaptive triboelectric vibration sensor for high-fidelity and continuous monitoring of mechanical vibration conditions is proposed. The sensor is entirely composed of flexible materials. It consists of a conductive sponge-silicone layer and a fluorinated ethylene propylene film. It can detect vibration acceleration of 5 to 50 m s-2 and vibration frequency of 10 to 100Hz. It has strong robustness and stability, and the output performance barely changes after the durability test of 168000 working cycles. Additionally, the flexible sensor can work even when the detection point of the mechanical equipment is curved, and the linear fit of the output voltage and acceleration is very close to that when the detection point is flat. Finally, it can be applied to monitoring the working condition of blower and vehicle engine, and can transmit vibration signal to mobile phone application through Wi-Fi module for real-time monitoring. The flexible triboelectric vibration sensor is expected to provide a practical paradigm for smart, green, and sustainable wireless sensor system in the era of Internet of Things.

The study of hydraulic machinery condition monitoring based on anomaly detection and fault diagnosis

The existing intelligent monitoring methods are hard to apply directly to hydraulic machinery due to fault data collection difficulties and the uncertainty of fault occurrence. To monitor the hydraulic machinery effectively, we design a novel feature extraction method called TFF_VMD and propose a comprehensive framework based on support vector data description (SVDD) and support vector machine (SVM), dividing the monitoring task into anomaly detection and fault identification. A pump test bench is built for data collection and method validation. Meanwhile, one online bearing dataset is used to pre-train SVM. The proposed method achieves satisfying results on three datasets, getting the accuracy of anomaly detection at 0.891, 0.999, and 0.995, respectively, getting mean and standard deviation of fault diagnosis accuracy at 0.954 ± 0.05, 0.96 ± 0.04, and 0.98 ± 0.03. Results show that TFF_VMD is valuable for SVDD and SVM, and the proposed framework can preliminarily achieve early warning and diagnosis of mechanical and hydraulic faults.

Dynamic modeling and force-vibration mapping mechanism construction of rolling bearings considering cage flexibility and local faults

Rolling bearings are one of important components of rotating machinery transmissions and thus its health condition is important for the safety of machinery. Thus, rolling bearing vibration analysis is critical for bearing health condition monitoring. Current bearing vibration analysis ignores multiple compound influencing factors for dynamic modeling. Hence, a more accurate bearing dynamic model, considering multiple factors including cage flexibility, time-varying displacement excitations caused by local defects and elastohydrodynamic (EHD) lubrication, is proposed to accurately reflect force states among bearing elements and vibration responses of bearings. Furthermore, the force states and vibration responses of bearings with localized defects in the proposed model are analyzed in details: (1) the main excitation source of the dynamic model under faulty conditions is identified as the contact force between the rolling element and the raceway by analyzing the vibration signals and main physical variables (the contact forces and the friction forces) in numerical values and change trends; (2) the mapping mechanism between the main excitation source and the vibration response is constructed. The force states among bearing elements are detailedly analyzed in the proposed model and the relationship between the forces and bearing vibration responses is revealed, facilitating the bearing health condition monitoring and vibration control of machinery.

Triboelectric Nanogenerators for Preventive Health Monitoring.

With the improvement in life quality, the increased focus on health has expedited the rapid development of portable preventative-health-monitoring devices. As one of the most attractive sensing technologies, triboelectric nanogenerators (TENGs) are playing a more and more important role in wearable electronics, machinery condition monitoring, and Internet of Things (IoT) sensors. TENGs possess many advantages, such as ease of fabrication, cost-effectiveness, flexibility, material-selection variety, and the ability to collect low-frequency motion, offering a novel way to achieve health monitoring for human beings in various aspects. In this short review, we initially present the working modes of TENGs based on their applications in health monitoring. Subsequently, the applications of TENG-based preventive health monitoring are demonstrated for different abnormal conditions of human beings, including fall-down detection, respiration monitoring, fatigue monitoring, and arterial pulse monitoring for cardiovascular disease. Finally, the discussion summarizes the current limitations and future perspectives. This short review encapsulates the latest and most influential works on preventive health monitoring utilizing the triboelectric effect for human beings and provides hints and evidence for future research trends.

Toward efficient and accurate extraction of instantaneous frequency with chirplet transform and its applications in bearing fault diagnosis

In many engineering applications, the instantaneous frequency (IF) needs to be identified rapidly and precisely, especially in rotating machinery condition monitoring, where quasi-real-time analysis is a favorable option, and any error in shaft speed (identified from the instantaneous frequency) needs to be reduced to prevent it from accumulating with time. The modern time–frequency analysis (TFA) method provides a way for IF identification; however, sometimes, it still suffers from energy smearing and closely spaced frequency interference. In addition, many TFA methods based on the chirplet transform also present the problem of excessive computation when searching for the optimal parameters. To overcome these limitations, we propose the multi-extracting velocity-synchronous chirplet transform (MEVCT), an enhanced TFA method, in this paper. In the MEVCT, a refined chirp parameter search is developed first to reduce the computational cost while increasing time–frequency accuracy. Then, an iterative high-order IF estimator is constructed to allow the IF to achieve or approach the actual values even in highly nonlinear situations. After that, the obtained IF estimate is substituted into the simultaneous extraction operator to reduce energy dispersion further and eliminate non-reassigned points (NRPs). Simulated and experimental signal analyses demonstrate that MEVCT is able to eliminate NRPs completely. Furthermore, when handling synchronous multicomponent signals, the proposed method has higher IF accuracy with less computational cost and better anti-noise capability.

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.

In full swing: Analytical strategies for the conservation assessment of operating Industrial Heritage machinery

The condition assessment of Industrial Heritage machinery in operation is an open research subject of utmost importance for defining conservation and maintenance plans. The present work approaches this issue by proposing the study of Industrial Heritage machinery that includes the assessment of substrates, coatings, lubricants and operating mechanisms by applying analytical techniques currently used for heritage studies together with techniques employed in condition monitoring of modern machinery. An early 20th century steam engine from the Vale de Milhaços Gunpowder Factory (Portugal), in operation for demonstration purposes, was selected as a case study. The engine metallic alloys and coatings were studied by XRF and Raman spectroscopies, and the main properties of lubricants studied according to EN ISO and ASTM standard tests. Metallic particles present in the oil due to wear were assessed by XRF. The mechanisms were examined by gamma radiography, magnetoscopy, thermography and vibrational analysis.Data showed the use of distinct alloys according to the engine components' function similar to those reported for the same period. Green copper phthalocyanine and toluidine red identified as coatings’ pigments are typical from the late 20th century and thus ascribed to a previous repair. The properties of the fresh and used lubricants are slightly different but without evidence of a significant alteration that could endanger the machine's functioning. However, the presence of metallic particles in the oil suggested the existence of wear phenomena. The engine's operating speed (51 rpm) and the vapour temperature (≈154 °C) attained inside the cylinder were indirectly determined. The presence of cracks on the engine structure was assessed, and possible faults that can imperil the engine functioning were analysed. The vibrational analysis of equipment in operation revealed a lack of lubrication and wear on the feed pump journal bearings.

Adaptive Fault Components Extraction by Using an Optimized Weights Spectrum Based Index for Machinery Fault Diagnosis

Machinery condition monitoring and fault diagnosis has attracted much attention because it is beneficial to reducing maintenance costs and improving industrial profits. Adaptive fault components extraction (AFCE) is the most crucial step for machinery fault diagnosis, and its core is statistical indices. Existing statistical indices including kurtosis and correlated kurtosis are empirical statistical indices (ESIs), and they cannot exactly quantify fault-related information in signals and distinguish fault components from interferential components. Thus, the ESIs might be easily affected by random impulsive noise, low frequency components, etc. To solve this problem, a new statistical index named optimized weights spectrum based index (OWSI) is proposed in this paper. The OWSI satisfies two good properties to guarantee exact quantification of fault components and effectively distinguish interferential components. Moreover, a new OWSI-based methodology is proposed to realize AFCE, and it can be implemented with signal decomposition algorithms such as variational mode decomposition without needing careful parameters tuning. Bearing and gear real-world fault signals are studied to verify the effectiveness of the proposed methodology. Results show that the proposed methodology is superior to ESI-based methods including classic fast kurtogram and newly developed feature mode decomposition.

Multi-Node Feature Learning Network Based on Maximum Spectral Harmonics-to-Noise Ratio Deconvolution for Machine Condition Monitoring

Since the cyclostationarity in vibration signals is the key to judge the rotating machine health state, spectral harmonics-to-interference ratio (SHIR) has been used to construct single node feature learning network (SHIR-based blind deconvolution, BD-SHIR) to realize condition monitoring of machine. However, BD-SHIR still has several obvious limitations, including the need for prior fault information and the tendency to fall into local optimal solutions which will affect its feature learning and state monitoring performance. Therefore, this paper proposes a multi-node feature learning network based on BDSHIR, which does not rely on prior information, multi-node adaptive BDSHIR (MABD-SHIR). It uses each filter in the 1/3 binary tree filter bank to design each initial node (filter) in the learning network correspondingly, and introduces an adaptive fault characteristic frequency (FCF) detection process. Since this initialization can give some filters close to the fault resonant band, iterations starting from these nodes are more likely to detect the precise FCF and converge to the optimal solution. Finally, MABD-SHIR can obtain multiple local optimal solutions, among which the one with the largest SHIR is generally closer to the global optimal solution than the one obtained by BD-SHIR. Simulation and experimental data of defective rotating machinery verify the effectiveness of MABD-SHIR in machinery condition monitoring and fault feature learning. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the problems that the rotating machine condition monitoring based on BDSHIR is easy to prematurely fall into local optimal solution and requires prior fault information. These problems can be effectively solved by extending BDSHIR from single node structure to multi-node structure and introducing adaptive period detection technique (A-DPT). The core idea is to start iterations from multiple initial filters and to detect the FCF adaptively during each iteration. Multiple features conforming to filtered signals’ SHIR maximization can be finally learned. Therefore, in this paper, MABD-SHIR is proposed, which is equivalent to a multi-node feature learning network. Since the objective function of BD is usually non-convex, MABD-SHIR can obtain multiple local optimal solutions without requiring prior FCFs. This is more likely to achieve a global optimal solution than BDSHIR, which starts iteration from only one initial filter and ends up with one local optimal solution. In addition, the objective function of MABD-SHIR has lower mathematical complexity, which reduces the difficulty of optimization.

Predictive Maintenance for Construction Equipment using Artificial Intelligence and Machine Learning

Efficient maintenance of construction equipment plays an essential role in ensuring optimal functionality, reducing downtime, and enhancing safety on job sites. Playing Artificial Intelligence (AI) and Machine Learning (ML) techniques in construction equipment maintenance introduces a standard shift in traditional maintenance strategies. This paper explores the application of AI and ML algorithms in predictive maintenance, fault detection, and condition monitoring of heavy machinery used in construction. By harnessing data-driven insights, these technologies enable real-time monitoring of equipment health, facilitating timely identification of potential failures or performance degradation. Furthermore, the integration of AI-driven maintenance systems optimizes equipment utilization, minimizes unplanned downtime, and enhances overall operational efficiency. This research underscores the significance of AI and ML methodologies in developing construction equipment maintenance, paving the way for cost-effective, proactive, and predictive maintenance strategies in the construction industry

A bibliometric analysis on condition monitoring of machinery using IR Thermography

In order to extend the life of machinery, condition monitoring of equipment has become crucial in every industry or power plant. IR Thermography is a strong tool for condition monitoring since it can detect flaws earlier than other techniques. The most important technique for condition monitoring is infrared thermography. Machine condition monitoring has evolved along with machine maintenance, from manual maintenance through approaches involving the use of sensors to the now under discussion methods and techniques centered on shared automation, data processing, and exchange. The purpose of this paper is to give a brief summary of the academic work that has been done on using IR thermography to monitor the status of machinery. In order to present an up-to-date, content-based analysis that includes bibliometric performance analysis and systematic literature evaluation, this paper evaluates the body of current literature. The writers may concentrate on identifying the most important researchers, journals, and publications, as well as the most significant and influential researchers, thanks to the mix of literature review methodologies used. These conclusions have prompted the authors to outline research trends pertinent to the studied domain and, in addition, to identify potential future research goals from engineering viewpoints.

Research on Rotating Machinery Fault Diagnosis Based on an Improved Eulerian Video Motion Magnification.

Rotating machinery condition monitoring and fault diagnosis are important bases for maintenance decisions, as the vibrations generated during operation are usually imperceptible to the naked eye. Eulerian video motion magnification (EVMM) can reveal subtle changes and has been widely used in various fields such as medicine, structural analysis, and fault diagnosis, etc. However, the method has a bound relationship among three parameters: spatial wavelength, amplification factor, and displacement function, so it is necessary to adjust the parameters manually in practical applications. In this paper, on the basis of the original method, an automatic solution of spatial cutoff wavelength based on brightness is proposed. First, an input video is decomposed into image sequences, their RGB color spaces are transformed into HSV color spaces, and the Value channel image representing brightness is selected to automatically calculate the spatial cutoff frequency, and then the spatial cutoff wavelength is determined, and the motion magnification video in the specified frequency band is obtained by substituting it into the original method. Then, a publicly available video is taken as an example for simulation analysis. By comparing the time-brightness curves of the three videos (original video, motion magnification video obtained by the original method and the improved method), it is apparent that the proposed method exhibits the most significant brightness variation. Finally, taking an overhung rotor-bearing test device as the object, five conditions are set, respectively: normal, rotor unbalance, loosened anchor bolt of the bearing seat, compound fault, rotor misalignment. The proposed method is adopted to magnify the motion of the characteristic frequency bands including 1X frequency and 2X frequency. The results show that no obvious displacement is found in normal working conditions, and that the rotor unbalance fault has an overall axial shaking, the bearing seat at the loose place has an obvious vertical displacement, while the compound fault combines the both fault characteristics, and the rotor misalignment fault has an obvious axial displacement of the free-end bearing seat. The method proposed in this paper can automatically obtain the space cutoff wavelength, which solves the problem of defects arising from manually adjusting the parameters in the original method, and provides a new method for rotating machinery fault diagnosis and other fields of application.

Correlation warping radius tracking for condition monitoring of rolling bearings under varying operating conditions

Health indicators (HI) are crucial in early fault alarm and degradation monitoring of mechanical failures. In recent years, many HIs are developed and reported, however, a constructed HI which is more sensitive to incipient defect, robust to varying operating conditions and is interpretable to characterize the degradation process is still remain to be studied. To address this issue, a more powerful and effective HI applicable for nonstationary operating condition is studied in this paper. Inspired by chaos fractal theory (CFT) and slowly varying damage dynamics theory (SVDDT), this paper calculates the range of correlation warping radius (CWR) of each segment of signal through phase space reconstruction, and then finds that the boundary of correlation warping radius range (CWR-B) in normal conditions follows a fixed linear relationship. Through the application of this linear relationship, a new health monitoring indicator for vibration signals of rotating machinery has been proposed, through correlation warping radius tracking measurement (CWRTM). The proposed indicator not only has outstanding monitoring advantages for rotating machinery in a cyclo-stationary condition, but also applicable for machinery condition monitoring under variable operating conditions, achieving the unity of methods for condition monitoring in stationary and variable operating conditions. Compared with some typical health indicators, the experimental results on bearing run-to-failure dataset and wind turbine generator bearing field dataset show that the proposed indicator in this article is not only sensitive to early-stage fault, but also validated robust to varying operating conditions.

All elastomeric pillars-based triboelectric vibration sensor for self-powered broad range machinery condition monitoring

Industrial vibration monitoring is crucial for anticipating equipment failures and ensuring smooth machinery operations. To address this need, we propose a Siloxene-Ecoflex elastomeric macro-pillared self-powered triboelectric vibration sensor (SP-TVS) that offers exceptional detection capabilities, with an ultra-wide frequency range (0.01–15 kHz) and acceleration range (0.1–7.0 g). The SP-TVS combines a facile fabricated elastomeric macro-pillar of nanocomposite paired with a flexible thin film of styrene-ethylene-butylene-styrene (SEBS) to realize the TVS. The SP-TVS exhibits high-frequency response (up to 15 kHz) due to the random high elastic vibration behavior of the macro-pillars and demonstrates excellent acceleration sensitivity of 2.033 V/g (0.1–0.5 g), 1.455 V/g (0.5–2.0 g), and 1.116 V/g (2.0–7.0 g) at r2 = 0.99 in low, medium, and high acceleration ranges and enables subtle vibration amplitude detections. Finally, the SP-TVS has been successfully deployed on various machineries, employing a sophisticated Artificial Intelligence algorithm to process sensor data, performs self-powered machinery identification, vibration inspection, and fault monitoring, showcasing its significant potential for a wide range of industrial monitoring applications.

A novel method to repair missing vibration data in rolling bearing vibration signals based on improved optimized measurement matrix

Missing data, caused by many factors such as equipment short circuits or data cleaning, affect the accuracy of condition monitoring for rotating machinery. To improve the precision of missing data recovery, a compressed sensing-based vibration data repair method is developed. First, based on the Gaussian random matrix, an improved optimized measurement matrix (OMM) is proposed to accurately sample data. Then, a sparse representation of the vibration signal, through a discrete cosine transform, is utilized to make the noisy vibration signal sparse. Finally, the orthogonal matching pursuit algorithm is employed to reconstruct the missing signal. The effectiveness of the proposed method is verified by analyzing constant and variable speed time series of rolling bearings. Compared with other data repair methods, it is shown that the OMM has a higher repair precision at different loss rates.

Health indicator based on signal probability distribution measures for machinery condition monitoring

Health indicator (HI), which aims to make quantitative measures for machinery operating state at different degradation stages, is very critical in machinery condition monitoring. Some HIs from different aspects have been developed and reported in recent years. However, a preferable HI which is more robust to transient interferences, free of complicated model training and also sensitive to incipient defects in machinery condition monitoring still remains to be further investigated. To address these issues, a novel HI based on signal probability distribution measures is proposed in this paper. Firstly, characteristic parameters of the alpha stable distribution are preliminarily estimated based on the machinery degradation data, the consistency of which is quantitatively evaluated and optimized through the hypothesis test with a parameter calibration strategy. Afterwards, signal distribution models are accordingly constructed to describe the statistical characteristics of the machinery degradation data. On this basis, the deviation of the established signal distribution models between the current degradation state and the initial fault-free state is accordingly analyzed and quantified for machinery degradation assessment. Experimental validations by using simulated and industrial run-to-failure datasets demonstrate that the proposed HI can effectively recognize the state shift of the machinery during the degradation process and can be therefore applied for machinery condition monitoring.