Machinery Condition Research Articles

Recent progress and development trends of oil wear particle detection sensors applied in marine machinery condition monitoring

Abstract Oil wear particle can offer wear condition for marine machinery. The shape, size and composition of wear particle can supply important information for diagnosing the type and severity of marine machinery faults. Through oil wear particle detection, potential marine machinery fault diagnosis can be found early, thereby reducing the risk of marine machinery failure diagnosis and improving the reliability and service life of marine machinery. This review summarizes the detection methods, technical characteristics and research progress of oil wear particle detection sensors applied in marine machinery condition monitoring. Oil wear particle detection sensors are divided into impedance-based particle detection method and non-impedance-based particle detection method. Impedance-based method includes inductance, capacitance and resistance principles. Non-impedance-based method includes image processing, optical and acoustic principles. Finally, it points out the future development trends and novel ideas for the oil wear particle detection sensors employed in marine machinery.

Weighted squared envelope nonlinear Gini index and its application to rotating machinery condition monitoring

Abstract Sparsity measures (SMs) are effective tools for rotating machinery condition monitoring. However, under complex operating conditions, existing SMs often exhibit significant fluctuations, making it difficult to accurately detect early faults and monotonically represent the degradation processes. To tackle these challenges, a novel health indicator (HI) named the weighted squared envelope nonlinear Gini index (WSENGI) is proposed in this paper. Firstly, by introducing a nonlinearly increasing weight sequence inspired by the sigmoid function and the quadratic function-based quasi-arithmetic mean (QAM), a novel SM, the nonlinear Gini index (NGI), is developed based on the ratio of different QAMs framework. Building upon this foundation, the WSENGI is further constructed for condition monitoring by incorporating the weighted squared envelope into the NGI. When applied to two bearing run-to-failure datasets, the proposed HI shows improved sensitivity to early fault features and is able to depict the degradation processes monotonically, demonstrating notable advantages in condition monitoring.

The Fusiongram: a periodic weak fault feature extraction strategy and its application in bearing fault diagnosis

Abstract The weak periodic transient impact responses caused by localized defects in rolling bearings are often obscured by complex interferences, such as white noise, random transient impact responses, and periodic responses from system operations. Meanwhile, the fault feature information contributing to damage detection may be distributed across different frequency bands in the vibration signal. Therefore, under the influence of complex interference, it is a challenging problem regarding how to 1) accurately select the frequency bands containing rich fault feature information, 2) effectively extract damage information from different frequency bands, and thereby, 3) successfully achieve fault diagnosis for machinery condition monitoring. To address these issues, this research introduces a novel signal processing strategy, termed as Fusiongram, for extracting weak periodic fault features amidst the influence of complex interferences. Firstly, the method of complementary hierarchical decomposition is proposed, in which the signal is decomposed into multiple components with overlapping frequency contents. Then, an index with interference resistance is constructed to select the components carrying rich damage feature information. Finally, the adaptive threshold denoising and multicomponent normalized averaging techniques are employed to fuse the information from the squared envelope spectra of the selected components, thus obtaining the reconstructed squared envelope spectra for fault diagnosis. The Fusiongram is able to achieve the goal of weak fault feature extraction from signals with complex interference. The analysis results of numerical simulation and experimental testing verify the effectiveness and advantages of the proposed strategy.

A Spectral-Based Blade Fault Detection in Shot Blast Machines with XGBoost and Feature Importance

The optimal functionality and dependability of mechanical systems are important for the sustained productivity and operational reliability of industrial machinery, and have a direct impact on its longevity and profitability. Therefore, the failure of a mechanical system or any of its components would be detrimental to production continuity and availability. Consequently, this study proposes a robust diagnostic framework for analyzing the blade conditions of shot blast industrial machinery. The framework explores the spectral characteristics of the vibration signals generated by the industrial shot blast for discriminative feature excitement. Furthermore, a peak detection algorithm is introduced to identify and extract the unique features present in the peak magnitudes of each signal spectrum. A feature importance algorithm is then deployed as the feature selection tool, and these selected features are fed into ten machine learning classifiers (MLCs), with extreme gradient boosting (XGBoost (version 2.1.1)) as the core classifier. The results show that the XGBoost classifier achieved the best accuracy of 98.05%, with a cost-efficient computational cost of 0.83 s. Other global assessment metrics were also implemented in the study to further validate the model.

Experimental Investigation Using Robust Deep VMD-ICA and 1D-CNN for Condition Monitoring of Roller Element Bearing

Abstract A rotor-bearing system experiences numerous vibrations during the operation that frequently degrade performance and endanger operational safety. Roller bearing failure has significant consequences, leading to downtime or even a complete outage of rotating machinery. It is crucial to detect and diagnose incipient bearing defects promptly to ensure optimal operation of the machinery and minimize potential disruptions to the process. Deep Independent Component Analysis is a necessity used in modern condition monitoring to detect bearing failure earlier than it occurs. To address this issue the feasibility of utilizing Deep Independent Component Analysis (ICA) method based on Variational Modal Decomposition (VMD) with one-dimensional convolutional neural network (1D-CNN) to diagnose incipient bearing defect. Fast Fourier Techniques are utilized to extract the vibration signatures of artificially damaged bearings on a newly built test bed. VMD addresses to minimize data noise, allowing data to decompose into various sub-datasets for extraction of incipient defect features. With weak defect characteristic signal and noise interference, the Deep VMD-ICA model and 1D-CNN simplicity improved the accuracy of diagnosis corresponding to the experimental results. Moreover, Deep VMD-ICA with 1D-CNN has additionally demonstrated strong performance in comparison to experimental results and is useful for monitoring the condition of industrial machinery. The results reveal that this fault diagnosis approach is reliable, with a diagnostic accuracy of 98.93% for bearing faults.

Editorial for Special Issue “10th Anniversary of Machines—Feature Papers in Fault Diagnosis and Prognosis”

Machinery condition monitoring is important in industries [...]

Configuration design methodof hybrid electric agricultural machinery system based on layered two-dimensional matrix

The working environment and objects of agricultural machinery are different from those of automobiles, andagricultural machinery is greatly affected by the working environment and working conditions, and the power output systemismore complex. Agricultural machinery not only has drive output, but also PTO output and hydraulic output, whichtogetherconstitute the output system of agricultural machinery. Agricultural machinery conditions can be dividedintoroadtransportation working conditions and field operation working conditions. The working conditions of agricultural machinerycan be divided into different load conditions according to the different traction tools and whether the hydraulic andPTOwork,such as ploughing, rotary tillage, fertilization, and transportation. Therefore, developing hybrid electric agricultural machinerysystems that are suitable for various complex working conditions holds great theoretical significance and practical value. Giventhe complex working conditions of agricultural machinery systems in agricultural work and the intricate challengesindesigning hybrid agricultural machinery systems. In this paper, the two-dimensional matrix is used to represent thephysicalstructure and dynamics of the multi-channel power output agricultural mechanism. A hierarchical two-dimensional matrixmethod for the generation and screening of hybrid electric agricultural machinery systems with multi-power output powerisproposed. The components of agricultural machinery are divided into an input layer and an output layer, and these componentsare coded and defined, and then transformed into a matrix. The hierarchical two-dimensional matrix method is usedtogenerateand screen the hybrid electric agricultural mechanism type. Through the stratification of the matrix, the complexityoftheconfiguration generation is reduced, and the constraints are applied to the basic screening of the generated configurations. Therationality of the configurations obtained after generation and screening is verified by Simulink simulation. The resultsshowthat the configuration screened by this method can meet the performance requirements of agricultural machinery.

Low-Cost and Paper-Based Micro-Electromechanical Systems Sensor for the Vibration Monitoring of Shield Cutters.

Vibration sensors are widely used in many fields like industry, agriculture, military, medicine, environment, etc. However, due to the speedy upgrading, most sensors composed of rigid or even toxic materials cause pollution to the environment and give rise to an increased amount of electronic waste. To meet the requirement of green electronics, biodegradable materials are advocated to be used to develop vibration sensors. Herein, a vibration sensor is reported based on a strategy of pencil-drawing graphite on paper. Specifically, a repeated pencil-drawing process is carried out on paper with a zigzag-shaped framework and parallel microgrooves, to form a graphite coating, thus serving as a functional conductive layer for electromechanical signal conversion. To enhance the sensor's sensitivity to vibration, a mass is loaded in the center of the paper, so that higher oscillation amplitude could happen under vibrational excitation. In so doing, the paper-based sensor can respond to vibrations with a wide frequency range from 5 Hz to 1 kHz, and vibrations with a maximum acceleration of 10 g. The results demonstrate that the sensor can not only be utilized for monitoring vibrations generated by the knuckle-knocking of plastic plates or objects falling down but also can be used to detect vibration in areas such as the shield cut head to assess the working conditions of machinery. The paper-based MEMS vibration sensor exhibits merits like easy fabrication, low cost, and being environmentally friendly, which indicates its great application potential in vibration monitoring fields.

Horizontal rearrangement frequency domain chirplet transform: algorithm and applications

Abstract Based on the idea of optimal time–frequency analysis (TFA) theory, a time–frequency post-processing method called horizontal rearrangement frequency domain chirplet transform (HRFCT) is proposed. The proposed method is designed to analyze non-stationary transient signals with nonlinear group delay (GD), aiming at obtaining energy-concentrated and accurate time–frequency representation (TFR). First, based on the image characteristics of the constructed amplitude function, the selection criteria of GD are defined, that is, the local maximum of amplitude function and the local minimum of absolute value of derivative of amplitude function are calculated. Next, according to the calculated GD, the coefficients on the GD trajectory are adaptively extracted, and the coefficients that cause the TFR to be blurred are discarded. Finally, the analysis result of a two-component simulation signal demonstrates the satisfactory performance of the HRFCT, and compared with four advanced TFA methods, the competitiveness of the HRFCT is verified. Additionally, the application of the HRFCT in the processing of constant and variable speed bearing signals highlights its prospects in transient signal analysis and rotating machinery condition monitoring.

ОБЩИЙ ПОДХОД К РЕШЕНИЮ ЗАДАЧИ ОБЕСПЕЧЕНИЯ РАБОТО-СПОСОБНОСТИ ТРАКТОРОВ В АПК

Agricultural production is characterized by the seasonality of mechanized work in the cultivation of crops. In addition, the amount of wear on aggregates and equipment systems is determined, on the one hand, by natural and climatic conditions and, on the other, by the nomenclature of operations performed in the planned period, organizational, production and technical conditions in a particular farm. Tractor units and systems are interconnect with each other and interdependent on each other, i.e. the amount of wear of some parts affects the wear of others. In this regard, the purpose of the research is to develop a concept for ensuring the operability of tractors in the agro-industrial complex by optimizing the resource consumption of aggregates and systems, taking into account the conditions of their operation. The most promising direction for improving the efficiency of tractor operation is the development of measures to optimize the resource consumption of their units and systems, taking into account the operating conditions of machinery in the agro-industrial complex. To predict changes in the physical parameter of a part, unit, and tractor system for the planned period of a certain amount of work, a concept is introduced - an indicator of resource consumption, which determines the amount of change in the diagnostic parameter of the unit state per unit of work performed. The assessment of the tractor condition is carry out by the level of equipment resource consumption, which makes it possible to promptly respond to the state of technical operation of equipment, the conditions of their operation and develop measures to optimize resource consumption. The resource consumption level (Urr) of the tractor, theoretically, can vary from 0 to 1. In this case, Urr=0 it is assumed that the tractor is written off from the balance sheet of the farm or is missing; with Urr=1, it is assumed that the tractor is in good condition and all tractor parameters have nominal values. With known patterns of changes in the level of resource consumption for various types of agricultural operations, it becomes possible to plan the range of work for each tractor for the planned period, taking into account the optimal resource consumption of their units and systems.

Adaptive Imputation of Irregular Truncated Signals with Machine Learning

In modern advanced manufacturing systems, the use of smart sensors and other Internet of Things (IoT) technology to provide real-time feedback to operators about the condition of various machinery or other equipment is prevalent. A notable issue in such IoT-based advanced manufacturing systems is the problem of connectivity, where a dropped Internet connection can lead to the loss of important condition data from a machine. Such gaps in the data, which we call irregular truncated signals, can lead to incorrect assumptions about the status of a machine and other flawed decision-making processes. This paper presents an adaptive data imputation framework based on machine learning (ML) algorithms to assess whether the missing data in a signal is missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR) and automatically select an appropriate ML-based data imputation model to deal with the missing data. Our results demonstrate the potential for applying ML algorithms to the challenge of irregularly truncated signals, as well as the capability of our adaptive framework to intelligently solve this issue.

Multi-constraint improved RS path planning method for unmanned rice direct seeding machine

Path planning is one of the key technologies that determines the efficiency and quality of field operations using autonomous agricultural machinery. To date, there has been extensive research on global coverage path planning for unmanned agricultural machinery within the working area; however, the issue of “encircling and edging” left by the machinery during headland turns is often overlooked. This study focuses on the operational conditions of unmanned agricultural machinery in the rice fields of southern China. The path planning problem is abstracted under physical constraints, such as the space at the field headland, the agricultural environment (e.g., types of field ridges), the turning characteristics of the machinery, turning radius, working width, and minimum safety distance, into mathematical constraints. This yields an optimized path planning model and sequence combination. Based on the improved Reeds–Shepp curve, an en-circling and edging path planning algorithm is designed. The planned path for the unmanned agricultural machinery from start to finish consists only of arcs and straight lines, suitable for the right-angle turning, reversing, and 180° U-turn operations required for rice field machinery, thus significantly improving the coverage of field edges and missed areas at the headland. A rice direct-seeding machine was used as a test subject for encircling and edging field trials, with tests conducted in rectangular, trapezoidal, and irregular quadrilateral rice fields. The results show that, for rectangular plots, the full-field coverage rate of rice direct-seeding operations can reach 96.44%; for trapezoidal plots, the coverage rate is 95.47%; and for irregular quadrilateral fields, the coverage rate is 95.69%. These results are promising, indicating that the encircling and edging path planning based on the improved Reeds–Shepp curve can effectively increase the full-field coverage rate of unmanned agricultural machinery, improving work quality and meeting the needs of modernized, intelligent agricultural machinery operations.

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.

High Performance and Stackable Trampoline Like‐Triboelectric Vibration Energy Harvester for In‐Situ Powering Sensor Node with Data Wirelessly Transmitted Over 1000‐m

AbstractAddressing the power supply challenges of wireless sensor nodes is pivotal for advancing the development of the Internet of Things (IoT). This work proposes a high performance and stackable trampoline like‐triboelectric vibration energy harvester (T‐TVEH) that can efficiently harvest ultra‐wideband vibrational energy for in‐situ powering of sensor nodes. The unique structural design and material selection enables T‐TVEH to represent a breakthrough in terms of both working bandwidth and power density compared to recent research efforts of vibration energy harvesting. Specifically, the working bandwidth and the peak power density of T‐TVEH is 192 Hz and 5.9 W m−2, which are higher than previous related studies by 156% and 59.2%, respectively. Based on the excellent performance of the T‐TVEH, a wireless sensor node for monitoring machinery condition is constructed. Temperature, humidity, and frequency information are successfully acquired and transmitted to 1000‐m through the wireless sensor node, which is nine times improved compared to related studies. Meanwhile, it achieves fully self‐powered wireless operation monitoring and abnormal alarm on a real ship's marine diesel engine. Overall, this study proposed an innovative solution for in‐situ power supply of wireless sensor nodes, which has broad application prospects in the field of the IOT.

Leasing in Russian agricultural economy: current stage of development

The purpose of the study is to analyze leasing sustainable development as the main investment mechanism of the Russian agrarian sector technical re-equipment. The author uses general scientific methods (analysis and synthesis, economic and statistical methods, graphical methods, etc.). The current state of the domestic agrarian sector technical equipment, the development of agricultural machinery leasing market development, issues and prospects for the use of agrarian leasing at the present stage have been considered. Considering the current situation in the domestic market, decreased prices for agricultural products and outstripping growth of prices for equipment, domestic agricultural producers need systemic state support to maintain the machinery and tractor fleet renewal pace. In conditions of agricultural machinery and investment resources for its reproduction deficit, leasing is one of the effective tools in solving the technical potential of the agricultural sector technical potential strengthening issues. The main promising areas of leasing mechanisms application, contributing to technical re-equipment acceleration in the agricultural sector have been proposed (sustainable development projects, national “green taxonomy”, “green” bonds; agricultural machinery market digitalization development; use of leasing mechanisms for the machinery and technology complexes development as a modern form of joint machinery use in agriculture). The proposed areas will ensure systemic development and acceleration of Russian agrarian sector technical re-equipment.

A Fully Programmable DAQ Board of Vibrational Signals from IEPE Sensors: Hardware and Software Design, Performance Analysis

Vibration analysis is commonly used to assess machinery conditions, earthquake detection, and structural monitoring. Commercially available DAQs (Data Acquisition Systems) feature high costs and limited versatility in terms of end-user hardware/firmware customization, making it difficult to adapt them to the input signal features and add supplementary functionalities. Hence, this research aims to develop a custom acquisition board for detecting vibration signals via IEPE (Integrated Electronic Piezoelectric) sensors, considering the limitations of commercially available systems, and building upon solutions found in the literature. The DAQ board was intended for remote vibration monitoring of infrastructure and machinery for industrial applications, allowing the implementation of predictive maintenance strategies. The proposed DAQ board has two independent and fully configurable channels, which can be set for acquiring signals from IEPE sensors or generic voltage sources. The DAQ board relies on the STM32F401 microcontroller to manage the acquisition from high-speed ADCs, process data, and store them in mass memory (SD card). During acquisition, the DAQ implements a batch acquisition strategy based on a buffer flash memory for temporarily storing ADCs data, which are iteratively poured into mass memory. Also, the board has Bluetooth connectivity to transmit acquired data and receive commands remotely. A prototype of the DAQ board was developed and tested with several waveforms, including vibration signals. The tests showed that the board can acquire vibration signals and compute the FFT onboard. The DAQ demonstrated a good balance between performance, accuracy, flexibility, and cost, making it suitable for several industrial applications and allowing for scalability and integration potential.

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.

Monitoring the reliability of agricultural machinery using information and diagnostic tools

The article presents an analysis and justifi cation of the need to improve approaches to diagnosis. Examples of experimental developments are presented — digital information and diagnostic tools for assessing the technical condition of agricultural machinery.