Condition Monitoring Applications Research Articles

Time-Series Feature Extraction by Return Map Analysis and Its Application to Bearing-Fault Detection

Developing new features for time-series characterization is a current challenge in data science and machine learning. In this paper, we propose a new metric based on a simple and efficient algorithm, namely, the return map. The return map analysis is well established in the field of non-linear dynamics, in particular, for fitting the parameters of a chaotic system from a waveform, or to attack a chaotic communication channel. We show that our metrics work well for both non-linear dynamics and time-series feature extraction problems in the field of machine learning. In an experiment aiming to classify vibration signals of normal and damaged bearings, we compare our method with two other methods that reported to have excellent accuracy, based on entropy and statistical feature distribution, respectively. We show that our method achieves higher accuracy with almost the lowest time costs, which was confirmed in experiments with two different datasets containing three main classes of bearings: normal, with inner race faults, and with outer race faults, having different damage origins and recorded in various conditions. In particular, for the dataset supplied by Case Western Reserve University, our method reached an accuracy of 100% at signals of 5000 sample points length, with a total time of 0.4 s required for feature estimation, while the entropy-based method reached an accuracy of 95% with a time of 100 s, and a statistical feature distribution method reached an accuracy of 93% with a total time of 1.9 s. Results show that the developed method is better suited to real-time bearing condition monitoring applications than most of the methods reported to date.

Real-Time Monitoring of Cable Sag and Overhead Power Line Parameters Based on a Distributed Sensor Network and Implementation in a Web Server and IoT.

Based on the need for real-time sag monitoring of Overhead Power Lines (OPL) for electricity transmission, this article presents the implementation of a hardware and software system for online monitoring of OPL cables. The mathematical model based on differential equations and the methods of algorithmic calculation of OPL cable sag are presented. Considering that, based on the mathematical model presented, the calculation of cable sag can be done in different ways depending on the sensors used, and the presented application uses a variety of sensors. Therefore, a direct calculation is made using one of the different methods. Subsequently, the verification relations are highlighted directly, and in return, the calculation by the alternative method, which uses another group of sensors, generates both a verification of the calculation and the functionality of the sensors, thus obtaining a defect observer of the sensors. The hardware architecture of the OPL cable online monitoring application is presented, together with the main characteristics of the sensors and communication equipment used. The configurations required to transmit data using the ModBUS and ZigBee protocols are also presented. The main software modules of the OPL cable condition monitoring application are described, which ensure the monitoring of the main parameters of the power line and the visualisation of the results both on the electricity provider's intranet using a web server and MySQL database, and on the Internet using an Internet of Things (IoT) server. This categorisation of the data visualisation mode is done in such a way as to ensure a high level of cyber security. Also, the global accuracy of the entire OPL cable sag calculus system is estimated at 0.1%. Starting from the mathematical model of the OPL cable sag calculation, it goes through the stages of creating such a monitoring system, from the numerical simulations carried out using Matlab to the real-time implementation of this monitoring application using Laboratory Virtual Instrument Engineering Workbench (LabVIEW).

Defect Detection in Synthetic Fibre Ropes using Detectron2 Framework

Fibre ropes with the latest technology have emerged as an appealing alternative to steel ropes for offshore industries due to their lightweight and high tensile strength. At the same time, frequent inspection of these ropes is essential to ensure the proper functioning and safety of the entire system. The development of deep learning (DL) models in condition monitoring (CM) applications offers a simpler and more effective approach for defect detection in synthetic fibre ropes (SFRs). The present paper investigates the performance of Detectron2, a state-of-the-art library for defect detection andinstance segmentation. Detectron2 with Mask R-CNN architecture is used for segmenting defects in SFRs. Mask R-CNN with various backbone configurations has been trained and tested on an experimentally obtained dataset comprising 1,803 high-dimensional images containing seven damage classes (placking high, placking medium, placking low, compression, core out, chafing, and normal respectively) for SFRs. By leveraging the capabilities of Detectron2, this study aims to develop an automated and efficient method for detecting defects in SFRs, enhancing the inspection process, and ensuring the safety of the fibre ropes.

CNN-Based Machine Tool Monitoring with STFT Image Analysis

Abstract: In the realm of machine tool operation, effective condition monitoring holds predominant importance to ensure operational reliability and safety. Leveraging deep learning methodologies, particularly convolutional neural networks (CNN), for defect identification has gained significant attention. However, inherent challenges persist, including the extraction of salient features and potential information loss while extracting the features from raw vibration signals. In response, this study proposes an intelligent approach for condition monitoring in machine tools, integrating short-time Fourier transform and convolutional neural networks (STFT-CNN). The process entails using STFT to convert one-dimensional vibration signals into time-frequency pictures, which are then fed into the STFT-CNN model to acquire and identify fault features. Furthermore, the study explores optimizing STFT parameters, such as window type, window width, and translation overlap width, across various typical window functions to improve the effectiveness of the transformation process. Within the STFT-CNN model, the utilization of stacked double convolutional layers aims to augment the model's nonlinear expression capacity, thereby facilitating robust fault diagnosis capabilities in machine tool condition monitoring applications

A proposed tacho-less order tracking technique suitable for railway vehicle condition monitoring applications

To perform a condition monitoring for rotating machines with variable running speed, order tracking is considered as an effective technique suitable for their non-stationary vibration signals. A tachometer signal is necessary to perform a computed order tracking (COT) technique; however, in some applications it's not possible to add tachometer hardware due to its complexity or shortage of power. To improve the applicability of order tracking techniques, some tacho-less order tracking techniques have been introduced in the last few years. These techniques are working off-line by inspecting the vibration signals in time-frequency domain to extract the machine rotating speed. In this paper, a new tacho-less technique suitable for continuous structural health monitoring applications of railway vehicles, is proposed. In the presented approach, a coarse angular speed of the railway axle is estimated firstly using estimation train speed. Then this coarse angular speed is used to improve the applicability of the traditional tacho-less techniques to be able to work online. The train speed is estimated from two acceleration signals obtained from vertical vibration of axle-boxes from same side of train. The sequential properties of the two signals are used to estimate the delay time of one signal with respect to the other one by means of cross-correlation function. By knowing the horizontal distance between the two sensors collected the vibration signals, the train speed can be estimated. This modification of tacho-less technique improves the effectiveness and robustness of the traditional maxima tracking strategy to be performed automatically. Thereafter, signal processing technique based on the computed order tracking as well as averaging technique can be applied to extract the useful information from noisy and non-stationary signals. This technique has been proposed in a framework of a project investigating crack detection using vibration measurements, therefore, vibration signals of cracked axle have been processed using tacho-based method and using the proposed tacho-less technique and a good agreement has been found between the two techniques.

An improved Cauchy robust correction-sage Husa extended Kalman filtering algorithm for high-precision SOC estimation of Lithium-ion batteries in new energy vehicles

The accurate estimation of battery State of Charge (SOC) is a key technology in the research of electric vehicle battery management systems. In order to solve the problem of inaccurate noise estimation in nonlinear systems, an improved Cauchy robust correction-Sage Husa extended Kalman filtering (CRC-SHEKF) algorithm is proposed for high-precision SOC estimation of lithium-ion batteries in new energy vehicles. Considering the polarization effect of the battery, the FFRLS algorithm is used for online parameter identification of the Dual Polarization model. Using robust data correction methods, the Cauchy robust function is simplified for real-time correction of the covariance matrix Q of system state noise and the covariance matrix R of the observed noise in the filtering process and combined with SHEKF for SOC estimation. The experimental results show that under different temperature conditions and complex working conditions, the proposed CRC-SHEKF algorithm has the minimum mean absolute error (MAE), root mean square error (RMSE), and maximum error (MAX). Under the condition of the Beijing bus dynamic stress test (BBDST) at 15 °C, the MAE, RMSE, and MAX of the CRC-SHEKF algorithm are 0.392 %, 0.716 %, and 0.945 %, with the computing time of only 4.839 s. The algorithm proposed in this article has high accuracy and robustness, and has practical application value, providing a reference for the application of lithium battery condition monitoring.

Laser-Structured Thin Film Sensor Technology for Sliding Bearings in Internal Combustion Engines

Abstract Global issues such as climate change, environmental pollution, and conservation of resources require manufacturers of internal combustion engines (ICE) to achieve substantially reduced emissions of greenhouse gases and pollutants as well as increased engine efficiency and durability. Condition monitoring and predictive maintenance approaches for sliding bearings in ICEs are key tools for increasing engine durability and saving resources by exploiting more of the useful lifetime of a bearing while avoiding critical engine operation caused by bearing wear and failure. These approaches require appropriate measurement technology capable of acquiring informative parameters that reflect the current condition of the bearings while withstanding the high temperatures and mechanical loads encountered inside the engine and accommodating spatial constraints. This paper deals with research work related to advanced sensor technology that is highly integrated into sliding bearings so that information is obtained nearly directly from relevant areas such as the bearing running layer and the lubrication gap. An isolated, sputtered sensor layer with a thickness of a few micrometers is employed in combination with a laser structuring process to form the desired thin film sensor structure below the bearing running surface. While several measurement parameters and corresponding sensor types are conceivable, this paper focuses on temperature and strain measurements that rely on a change in the electrical resistance of the sensor layer material. Promising sensor layouts and positions targeted for use in condition monitoring applications in ICEs are elaborated in detail. Developments and challenges in implementing the sensor technology concept—in particular with regard to the process of manufacturing the sensor as well as the wire contacting—are outlined in depth. The paper concludes by presenting measurement results obtained with this sensor technology at lab scale as well as an outlook toward implementing the instrumented bearings in ICEs.

Attention-enhanced conditional-diffusion-based data synthesis for data augmentation in machine fault diagnosis

Data scarcity and class imbalance are pervasive challenges in machine fault diagnosis, impeding the development and broad adaptation of accurate and reliable deep-learning-based fault detection systems. To address these issues, we propose a novel attention-enhanced conditioning-guided diffusion-based approach for synthesizing additional training data. By generating noisy motor-current signals and employing a diffusion process to reduce noise levels, we obtain synthetic data that closely resembles the statistical properties and patterns of real-world data. The quality of the synthesized samples is assessed using a four-layer, pre-trained convolutional neural network, achieving an impressive classification rate of 96.39%. This demonstrates the effectiveness of our attention-enhanced conditioned diffusion-based model in synthesizing critical features for fault detection, making it well-suited for the introduced fault detection task. To further investigate the quality of the synthesized data samples, we implement a specific training strategy that uses the generated samples as training data. The trained diffusion model generates samples per class, which are then used to train a simple four-layer convolutional neural network for fault classification. Remarkably, the model achieves an accuracy of 98.96% when evaluated with real inverter signals, indicating that the generated samples effectively capture the distribution of real data. For further evaluation, multiple scenarios are created confirming the value of the additionally synthesized samples for real-world bearing fault detection. Our approach presents a well-suited solution to the data scarcity and class imbalance problem in machine fault diagnosis. The proposed method enhances the accuracy and reliability of fault diagnosis systems, offering valuable insights for various real-world condition monitoring applications.

Ion-sensitive field effect transistor biosensors for biomarker detection: current progress and challenges.

The ion-sensitive field effect transistor (ISFET) has emerged as a crucial sensor device, owing to its numerous benefits such as label-free operation, miniaturization, high sensitivity, and rapid response time. Currently, ISFET technology excels in detecting ions, nucleic acids, proteins, and cellular components, with widespread applications in early disease screening, condition monitoring, and drug analysis. Recent advancements in sensing techniques, coupled with breakthroughs in nanomaterials and microelectronics, have significantly improved sensor performance. These developments are steering ISFETs toward a promising future characterized by enhanced sensitivity, seamless integration, and multifaceted detection capabilities. This review explores the structure and operational principles of ISFETs, highlighting recent research in ISFET biosensors for biomarker detection. It also examines the limitations of these sensors, proposes potential solutions, and anticipates their future trajectory. This review aims to provide a valuable reference for advancing ISFETs in the field of biomarker measurement.

Design and Control for High-Reliability Power Electronics: State-of-the-Art and Future Trends

Electrification is a mega-trend in the modern economy, urged by the need for a more ecological and sustainable impact of new technologies, among many other merits related to the process of electrification. The widespread adoption of renewable energy, transportation electrification, energy storage systems and smart manufacturing is remarkable. As a result, power electronics has become the critical enabling technology for improving energy efficiency and sustainability. In recent years, the reliability of power electronic equipment has been under scrutiny, especially in mission-critical applications of power electronics converters. Recently, special attention has been paid to the development of lifetime models of the main electronic components, which could then be applied to probabilistic analysis based on the mission profile of system-level reliability. This prediction methodology has some limitations because it does not consider external factors and agents other than the wear itself. Redundancy has been the typical approach to improve the availability of mission critical power electronic systems, which could be associated with considerable extra cost. Fault-tolerant converters were proposed as a more cost-effective alternative solution that could also extend the service life. The maintenance cost could be reduced by using active thermal control, which allows the balancing of wear damage in modular power electronic converters and, consequently, the scheduling of maintenance events. A further step could be taken with the application of real-time condition monitoring of systems, which has evolved into the concepts of predictive and prescriptive maintenance that allows for prediction and mitigation of faults. These techniques are under active development and are expected to benefit from new methods and technologies from rapidly progressing areas of artificial intelligence and digital twin. This paper provides a detailed overview on the recent trends and advances in the field of high-reliability power electronics systems.

Digitization Workflow for Data Mining in Production Technology applied to a Feed Axis of a CNC Milling Machine

Condition monitoring and predictive maintenance applications receive ongoing scientific attention in production technology. Larger companies, especially machine and component manufacturers, already offer related products. Small and medium-sized enterprises (SMEs) in particular show interest in developing and offering solutions in this market themselves to gain economic advantages, to improve resource utilization of their machines or to be able to offer these advantages to their own customers. In the development process, however, they often encounter problems already in the digitization of the machines. The first hurdle is to obtain an analysis-capable data set. This is due to the fact that common and established general data mining development process models, such as CRISP-DM, do not focus on production technology, causing difficulties for engineers during deployment. A problem with existing process models is the limited practicality in the engineering domain due to restricted adaptability. In a previous paper, a guideline for engineers for data mining suitable digitization of production machines was developed in order to solve these problems. The related results were provided in the context of a project for condition monitoring of mixing machines. In this paper, the proposed method is applied to components of a 5-axis CNC milling machine in three different monitoring use cases. A complete workflow is presented, including effect analysis, sensor selection, formulation of predictive scenarios, data preparation, training of machine learning algorithms and vizualization. Data and documentation are provided alongside this publication.

Hybrid generator for efficient vibration energy harvesting and self-powered gear condition monitoring applications in industrial equipment

Hybrid generator for efficient vibration energy harvesting and self-powered gear condition monitoring applications in industrial equipment

Energy Management and Networking for Sensor-based Transmission Line State Monitoring

The transmission line condition monitoring sensor network is large in scale and difficult to maintain. Its long-chain network topology differs from general wireless sensor networks, so it is necessary to develop a self-organizing sensor network with self-energy harvesting. In this regard, a three-layer network model is designed. Three types of node models are constructed. The sensor nodes implement round-robin sleep/wake and energy management mechanisms, powered by self-harvested energy. Additionally, reliable routing between nodes is established. Simulation results show that the developed simulation model is suitable for the application of transmission line condition monitoring. The expected functions of each module have been realized, and the stable and reliable transmission of perceived data of key components of transmission lines has been achieved, thereby providing technical and data basis for the deployment of practical networks.

A New Approach to Study the Effect of Complexity on an External Gear Pump Model to Generate Data Source for AI-Based Condition Monitoring Application

The external gear pump, like any other hydraulic component, is vulnerable to failure, which may lead to downtime as well as the failure of other components linked to it, thereby causing production loss. Therefore, establishing a condition monitoring system is crucial in identifying failure at an early stage. Traditional condition monitoring approaches rely on experimental data that are collected by means of sensors. However, the sensors utilized in the experiments may have calibration issues, which lead to inaccurate measurements. The availability of experimental data is also limited as it is difficult and expensive to create and detect a fault in a component. Hence, it is essential to develop a simulation model that mimics the performance of the actual system. The data generated from the model can be utilized to create the data source required for automated condition monitoring. A new methodology based on a detailed geometric model for simulating the External Gear Pump is described and compared to two models analyzed in the authors’ previous work, namely Schlosser’s loss model and simple geometric model. In this paper, the three models are compared with experimental data and the method utilized for fault injection. Schlosser’s loss model, as well as the detailed geometric model, are found to be suitable in terms of validation; however, the latter is a better candidate in terms of fault injection. Hence, the detailed geometric model can be implemented as a tool to generate the data source for condition monitoring applications.

Co-design Model for Neuromorphic Technology Development in Rolling Element Bearing Condition Monitoring

This paper presents an end-to-end condition monitoring co-design model, from vibration measurement to anomaly detection, where conventional signal processing principles are combined with neuromorphic sensing and computing concepts to enable investigations of the potential improvements offered by brain-like information processing technologies.
 The use of machine learning in condition monitoring became increasingly popular for intelligent fault diagnosis in the last decade, taking advantage of the rapid developments in deep learning.
 However, the high computational cost of training and using deep neural networks prevents the use of such solutions for analysing the bulk of data generated by the resource constrained edge devices, i.e., the condition monitoring sensor systems, as only a minor fraction of data can be transmitted to the cloud or edge servers for analysis.
 There is an untapped potential to process this data and thereby improve intelligent fault diagnosis models using event-triggered sensing, spiking neural networks, and neuromorphic processors that substantially can improve the energy efficiency and capacity of embedded machine learning condition monitoring solutions.
 The proposed co-design model is evaluated on two use-cases involving rolling element bearing failures, one based on a labelled laboratory environment dataset, and one based on a wind turbine drivetrain bearing failure representing a real-world scenario with stochastic changes of machine state and unknown labels of the bearing condition.
 By adjusting co-design parameters, the resulting hybrid conventional/neuromorphic model show a comparable accuracy in detection performance for the laboratory dataset compared to the state-of-the-art reported in the literature.
 Similarly, for the wind turbine drivetrain dataset a bearing fault detection time comparable to that in previous work is obtained.
 This shows the successful implementation of a hybrid conventional/neuromorphic co-design model for condition monitoring applications, offering novel opportunities to investigate performance trade-offs and efficiency improvements enabled by neuromorphic technologies.

A Fourier-based explanation of 1D-CNNs for machine condition monitoring applications

Neural networks (NN) have generated extensive interest in the field of machine condition monitoring (MCM). Many applications are however adapting structures and approaches from fields where NNs are highly established (e.g., image classification). This is done despite many neural network layers presenting strong similarities to signal processing steps that are well established in MCM. This paper aims at providing a mathematical analysis of typical NN architectures and layers, with specific attention to similarities to common signal processing steps such as filtering, downsampling, and enveloping. The set of strong analogies between NN and traditional MCM tools enables this work to provide MCM-specific guidelines for the design and tuning of NN architectures for MCM-specific applications. Most MCM applications are often characterised by periodic and cyclostationary signals, a situation not frequently encountered in many other NN applications. Hence, the Fourier-based explanation of the typical NN layers will be specific to the solution of typical MCM problems (e.g., diagnostics of gears and bearings). The study is aligned with the current trends of explainable AI and physics informed neural networks (PINN), in the sense that it aims at producing mathematical rules for a rational design of the network, drawing on the expertise accumulated in decades of signal processing for MCM. This is however done without upsetting traditional NN architectures, but rather trying to provide clear reasons for the setting of network parameters. The analytical work will be verified with numerical tests, which will also enable practical considerations, and by application of the suggested guidelines to the popular Case Western Reserve University (CWRU) experimental dataset.

State-of-the-Art Techniques for Fault Diagnosis in Electrical Machines: Advancements and Future Directions

Electrical machines are prone to various faults and require constant monitoring to ensure safe and dependable functioning. A potential fault in electrical machinery results in unscheduled downtime, necessitating the prompt assessment of any abnormal circumstances in rotating electrical machines. This paper provides an in-depth analysis as well as the most recent trends in the application of condition monitoring and fault detection techniques in the disciplines of electrical machinery. It first investigates the evolution of traditional monitoring techniques, followed by signal-based techniques such as spectrum, vibration, and temperature analysis, and the most recent trends in its signal processing techniques for assessing faults. Then, it investigates and details the implementation and evolution of modern approaches that employ intelligence-based techniques such as neural networks and support vector machines. All these applicable and state-of-art techniques in condition monitoring and fault diagnosis aid in predictive maintenance and identification and have the highly reliable operation of a motor drive system. Furthermore, this paper focuses on the possible transformational impact of electrical machine condition monitoring by thoroughly analyzing each of the monitoring techniques, their corresponding pros and cons, their approaches, and their applicability. It offers strong and useful insights into proactive maintenance measures, improved operating efficiency, and specific recommendations for future applications in the field of diagnostics.

Theoretical Study of Platinum Doped Anatase (101) Surface on Detecting Decomposition Production of Polyvinyl Chloride

The gas sensitivity of platinum doped TiO2 (101) to two kinds on polyvinyl chloride decomposition production, CO and HCl, were explored based on density functional theory. The binding energy, adsorption distance, charge transfer, and density of states distribution were simulated and discussed. The results suggest that the O oriented mode is probably the best mode for the CO gas molecules adsorption on the Pt-TiO2 and H oriented mode for the HCl adsorption system. HCl and CO molecules obtain electrons from Pt doped TiO2 surface. At room temperature, the recovery time of CO and HCl is 1.9743 × 10-10, 1.2094 × 10-10 respectively. HCl is easier to dissociate from Pt doped TiO2 surface with a little lower energy barrier compared to CO. This work provides theoretical adsorption information of Pt-TiO2 as gas sensor material for HCl and CO detection in the application of online condition monitoring and defect diagnosis in the power cables.

Bridging the gap between AI and the industry — A study on bearing fault detection in PMSM-driven systems using CNN and inverter measurement

Deep-learning-based identification and diagnosis of faults in industrial assets has received much attention. It does not need a deep understanding of the target domain or complex signal-processing techniques for time-consuming feature identification and selection. The vast majority of approaches developed in this field rely on public data recorded in laboratory conditions with high-performance measurement equipment. Moreover, primarily vibration data is used for condition monitoring tasks, which is particularly sensitive to typical error patterns in rotating machinery. These conditions are difficult to maintain in industrial environments since using high-performance measurement systems would not be economically feasible. This paper demonstrates that modern deep learning can achieve extraordinary fault detection results with industrial standard low-cost measurement systems. We determine measurements taken from the industrial-sensory equipment that potentially capture the system’s fault patterns. Hereafter, we apply traditional failure detection and identification techniques to evaluate the suitability of the taken measurements for bearing fault detection. These techniques apply envelope analysis to reveal the fault patterns in the signals and a support vector machine to separate the fault classes. We can conclude that the used low-cost sensory equipment is capable to capture meaningful, fault-describing patterns. To improve the failure detection performance we are investigating a 2D, as well as 1D, convolutional neural network approach to identify the error patterns and classify the respective errors. We compare the deep-learning-based methods with the traditional methods. Furthermore, we assess which inverter signal carries the largest fault-describing information content. Experimental results indicate that the proposed deep learning methods outperform traditional fault diagnosis methods, hence, demonstrating the effectiveness in an industrial condition monitoring application.

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.