Condition Monitoring Applications Research Articles

A Brief Review of Recent Research on Reversible Francis Pump Turbines in Pumped Storage Plants

As the core for energy conversion in pumped storage plants, the pump turbine is also a key component in the process of building a clean power grid, owing to its fast and accurate load regulation. This paper introduces the current status of research and development of pump turbines from the perspectives of significance, design and optimization, operational performance, advanced research methods, etc. Internal and external characteristics such as transient flow evolution, structural vibration, flow-induced noise, etc., not only reflect operational performance (hydraulic, cavitation, sediment abrasion, and stability performance, etc.) but also directly affect the safe and efficient operation of the system. It is worth mentioning that the space-time evolution of internal and external characteristics is an emerging research direction, the results of which can be used to predict the operational conditions of pump turbines. Moreover, the development and application of intelligent condition monitoring and fault diagnosis aim to prevent failures and accidents in pumped storage plants.

The Role of National Unity and Political Bodies in Handling Potential Conflicts in West Java Province

This research aims to analyze the real role of the National Unity and Political Agency in Handling Potential Conflicts in West Java Province. This research uses qualitative research methods with data collection techniques in the form of interviews, observation and literature study, as well as data analysis using interactive models by Miles Huberman, Saldana (2014) and Role Theory according to Bruce J. Cohen (1992). The results of this research show that Bakesbangpol has issued West Java Governor Decree Number: 460.05/Kep.134-Bakesbangpol/2019 concerning the Integrated Team for Handling Regional Social Conflict in West Java Province as a follow-up to efforts to handle conflict. Conflict management carried out by Bakesbangpol in handling potential conflicts is through mediation and conciliation. In the context of successful conflict handling, West Java Bakesbangpol has applied the penta helix principle with a coordination, collaboration and synergy approach. Linked to role theory by Bruce J. Cohen (1992), Bakesbangpol has played a real role (Anacted Role) in resolving the polemic problem of establishing the construction of the Sang Hyang Hurip Santo Antonius Catholic Church in Bandung City. West Java Bakesbangpol also has a situation and condition monitoring application called "SIMBAKESBANGPOL" and a Whatsapp Group between Regency/City Bakesbangpol throughout West Java to find out up to date about conflict cases that occur in the West Java region as a whole.

Simultaneous sensing of axial strain and temperature based on a sensitivity-selectable all-fiber Lyot-like filter

In this paper, an all-fiber Lyot-like filter with tunable free spectral range (FSR) and selectable sensitivity is proposed and experimentally demonstrated for simultaneous sensing of axial strain and temperature. Compared to the traditional fiber-based Lyot filter, the Lyot-like filter has an additional single-mode fiber (SMF) based polarization controller (PC), which is placed between two sections of polarization-maintaining fiber (PMF). In our experiment, the tunable FSR is achieved by adjusting the PC at a special angle, and a small FSR value of 6.58 nm and a large FSR value of 18.49 nm are obtained, respectively. Moreover, the experimental results show that the small FSR has low axial strain and temperature sensitivity, while that of the large FSR is high. By detecting the wavelength shifts of the filter spectra dip points, the simultaneous sensing of axial strain and temperature can be realized easily using a dual-parameter matrix. The proposed Lyot-like filter has the advantages of tunable FSR, selectable sensitivity, and simultaneous measurement of temperature and axial strain, which shows potential sensing applications in aerospace, chemical production, and bridge condition monitoring.

Self-supervised Learning Approach for Anomaly Detection in Rotating Machinery

Early fault detection in rotating machinery needs careful expert analysis of vibration data for monitoring a component state. Online methods that automatically set a threshold and raise an alarm when the vibration signature is anomalous are meant to efficiently manage key assets in a preventive maintenance plan. In recent years a focus has raised on data driven methods in parallel with the increasing attention towards machine learning and, particularly, deep learning models. In this regard, for rotating equipment components, an important aspect relates to labelled data scarcity for supervised training. On the other hand, the advent of the Internet of Things allows to gather data from multiple assets with relevant information on the asset state itself. Self-supervised learning methods in deep learning application are currently tackling this problem. Investigating Self-learning approaches to integrate domain knowledge and learn relevant features from unlabeled data is therefore important for condition monitoring applications. In this paper a methodology is proposed based on cycle consistency representation learning for training an embedder network on univariate unlabeled data. In order to learn a distance metric in the embedding space the original data are transformed to generate sequences of augmented inputs to enforce learnable pattern similarity in the augmented pairs. A differentiable cycle-consistency loss is chosen to maximize the numbers of augmented pairs in the learned embedding space that have minimum features distance. The pretext task in the described self-supervised setting aims to train a feature extractor for discriminating dissimilar samples in the embedding space by a distance metric and to provide a useful representation for down-stream tasks. The paper analyzes the performance of the approach for anomaly detection in rotating machinery. The methodology is tested on vibration data provided by the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati, considering different accelerated life test campaigns. The data were collected to monitor the fault development in bearings and the model shows how the learned embedding space discriminates effectively anomalous samples from normal ones in the degradation stages of the bearings.

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

Wind turbine condition monitoring dataset of Fraunhofer LBF

Fraunhofer wind turbine dataset contains monitoring data from a 750 W wind turbine, including accelerometers and tachometer, to capture structural response, bearing vibrations and rotational velocity. Additionally, temperatures, wind speed and wind direction have been measured, while weather conditions have been acquired from selected sources. Various damage scenarios, including mass imbalance, and aerodynamic imbalance as well as damages on bearings’ outer race, inner race and roller element have been implemented. The availability of time series data makes the dataset well suited for both machine learning and signal processing-based condition monitoring applications. The availability of heterogeneous sensors has created a dataset particularly suited for information fusion, data fusion, multi-sensor approaches, and holistic monitoring. Experiments were conducted in real-world conditions outside of a controlled laboratory environment, thereby introducing challenges such as variable rotor speed, noise, overloads, and other environmental factors. Consequently, the dataset is qualified for tasks involving uncertainty quantification and signal pre-processing. This document will detail the test equipment, experimental procedures, simulated damage cases and measurement parameters.

DFT insights on the adsorption and sensing performance of TMO-MoSSe for oil dissolved gases

The safe operation of oil-immersed transformers is crucial to the overall stability of the power system, making the prevention of partial discharges vitally important. This study proposes the use of MoSSe doped with TMOs (CuO, Ag2O, TiO2) to detect dissolved gases (CO, C2H2, CH4) in transformer oil. The adsorption of target gases on pristine and modified substrates is systematically studied using density functional theory (DFT). Given the unique sandwich structure of MoSSe, this paper explores the stability of the S-plane and Se-plane doping, ultimately selecting the S-plane for doping. Compared with the initial band gap of the substrate (1.598 eV), doping with CuO, Ag2O, and TiO2 reduces the band gap of MoSSe to 1.535, 0.677, and 0.752 eV, respectively. In terms of sensing performance, the improvement provided by the oxide particles on the substrate is confirmed by analyzing the geometric structure, density of states (DOS), differential charge density (DCD), and work function (WF) of the adsorption system. By comparing the adsorption parameters of the target gas adsorbed on both the doped and initial substrates, it is found that the metal oxides enhance the adsorption of the target gas on MoSSe. CH4 exhibits weak physical adsorption on the three modified substrates, with the order of adsorption capacity being: C2H2 > CO > CH4. The results of this study provide a theoretical basis for future applications in transformer condition monitoring and gas sensing.

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.

Investigation of Rotor Impact on FRA Signatures in Induction Motors with Different Poles and Connections

The non-destructive nature of the Frequency Response Analysis (FRA) test is one of the reasons for gaining interest in the application of condition monitoring in rotating machines. The FRA technique is highly sensitive. Any small changes in the motor will generate a distinct frequency response, enabling early detection of the defect. This paper presents an in-depth experimental investigation of the rotor impact on FRA signatures of three-phase induction motors with different numbers of poles and connections. The experimental analysis was carried out using a frequency response analyzer, from which two distinct sets of measurements were taken. 1st set of measurements was taken to observe the effect of rotor presence on a 2-pole motor connected in star and delta. The same measurements were then repeated for 2nd motor with 4 poles. The results obtained from both sets of measurements are compared and analyzed. To enhance the comprehension of the results, statistical indicators are utilized. The finding highlights that the presence of a rotor can greatly influence the FRA signature. This helps further understand the FRA in motor and assists in accurate interpretation.

Intelligent identification of key characteristic points and its application in electromagnet operation condition monitoring of operating mechanism

Intelligent identification of key characteristic points and its application in electromagnet operation condition monitoring of operating mechanism

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.