Showing papers on "Condition monitoring published in 2022"
TL;DR: A new method is proposed to extract multidirectional spatio-temporal features of SCADA data for wind turbine condition monitoring based on convolutional neural network and bidirectional gated recurrent unit with attention mechanism with better feasibility of practical wind energy application.
67 citations
TL;DR: The stochastic degradation model proposed for estimation of real-time fatigue damage in the components is based on a proven model-based approach which is tested under different drivetrain operations, namely normal, faulty and overload conditions.
60 citations
TL;DR: A theoretical basis and roadmap to further study or build MVCMFD-MTs using information from the machined surface texture is provided, and current challenges and potential research directions in nowadays intelligent manufacturing are discussed.
52 citations
TL;DR: In this paper , a digital twin (DT) condition monitoring approach for drivetrains on floating offshore wind turbines is presented, which consists of torsional dynamic model, online measurements and fatigue damage estimation which is used for remaining useful life (RUL) estimation.
48 citations
TL;DR: In this paper , a comprehensive review of machine vision-based condition monitoring and fault diagnosis of machine tools is presented, which aims to provide researchers and engineers with a theoretical basis and roadmap to further study or build MVCMFD-MTs using information from the machined surface texture.
42 citations
TL;DR: In this paper , a deep learning method with LSTM architectures combined with a one-class support vector machine (SVM) is used to separate abnormal data from normal vibration signals collected during an endurance test of a reduction gearbox and helicopter test flight data measured by multiple sensors situated at different locations of the aircraft.
41 citations
18 Jan 2022
TL;DR: In this paper , a systematic study of the works related to the topic was carried out, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies.
Abstract: Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas.
37 citations
TL;DR: Wang et al. as mentioned in this paper proposed a framework based on feature normalization, attention mechanism, and deep learning algorithms for tool wear monitoring and multi-step prediction, and the results showed that the proposed model has great advantages in efficiency and robustness compared with other data-driven models.
34 citations
TL;DR: A nonlinear gear-shaft-bearing-housing vibration model with fourteen degree of freedom is presented to investigate the vibration responses under the dynamic gear meshing force and progressively changed radial clearances at first, and indicator based on modulation signal bispectrum-sideband estimator (MSB-SE) was proposed.
32 citations
TL;DR: In this article , the authors provide an updated comprehensive review of the state-of-the-art condition monitoring technologies used for fault diagnosis and lifetime prognosis in wind turbines and thoroughly review the techniques and strategies available for wind turbine condition monitoring from signal-based to model-based perspectives.
Abstract: Wind turbines play an increasingly important role in renewable power generation. To ensure the efficient production and financial viability of wind power, it is crucial to maintain wind turbines’ reliability and availability (uptime) through advanced real-time condition monitoring technologies. Given their plurality and evolution, this article provides an updated comprehensive review of the state-of-the-art condition monitoring technologies used for fault diagnosis and lifetime prognosis in wind turbines. Specifically, this article presents the major fault and failure modes observed in wind turbines along with their root causes, and thoroughly reviews the techniques and strategies available for wind turbine condition monitoring from signal-based to model-based perspectives. In total, more than 390 references, mostly selected from recent journal articles, theses, and reports in the open literature, are compiled to assess as exhaustively as possible the past, current, and future research and development trends in this substantial and active investigation area.
32 citations
TL;DR: A novel condition monitoring and fault isolation system based on a covariate-adjusted preprocessing procedure to account for the various working conditions of the wind turbine, and constructs a global monitoring statistic based on all temperature variables contained in SCADA data.
Abstract: Condition monitoring of the wind turbine based on supervisory control and data acquisition (SCADA) data has attracted much attention in recent years. Nevertheless, there are some inherent challenges in SCADA data analysis, including the low sampling rate, time-varying working conditions of the wind turbine, and a lack of historical fault data. To solve these problems, this article develops a novel condition monitoring and fault isolation system. First, a covariate-adjusted preprocessing procedure is proposed to account for the various working conditions of the wind turbine. Next, we construct a global monitoring statistic based on all temperature variables contained in the SCADA data, with a view to monitoring the overall health status of the wind turbine. If an alarm is raised, we isolate the fault through a variable selection method without relying on expert knowledge or historical fault data. Simulation and real cases are provided to demonstrate the effectiveness of this system.
TL;DR: In this paper , a comprehensive overview for condition-monitoring technologies, including lubricating oil and vibration condition monitoring, and wear-repairing strategies for diesel engines is provided, with a brief discussion of shortcomings and plausible future trends for various techniques.
TL;DR: In this paper, a comprehensive overview for condition-monitoring technologies, including lubricating oil and vibration condition monitoring, and wear-repairing strategies for diesel engines is provided, with a brief discussion of shortcomings and plausible future trends for various techniques.
TL;DR: In this paper, a self-data-driven RUL prediction method for WTs considering continuously varying speeds is proposed, which is applicable in industrial cases where no sufficient failure event data is available.
TL;DR: In this paper , a five-step Chow's test-based computation procedure is proposed for condition monitoring of a wind turbine drivetrain with a nominal power of 2 MW using temperature-related SCADA data.
01 May 2022
TL;DR: The feasibility of machine learning techniques like DA in the field of TCM is confirmed and using Bayesian optimization algorithms to fine-tune the model is confirmed, making it industry ready.
Abstract: With the advent of Industry 4.0, which conceptualizes self-monitoring of rotating machine parts by adopting techniques like Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), data analytics, cloud computing, etc. The significant research area in predictive maintenance is Tool Condition Monitoring (TCM) as the tool condition affects the overall machining process and its economics. Lately, machine learning techniques are being used to classify the tool's condition in operation. These techniques are cost-saving and help industries with adopting future-proof solutions for their operations. One such technique called Discriminant analysis (DA) must be examined particularly for TCM. Owing to its less expensive computation and shorter run times, using them in TCM will ensure effective use of the cutting tool and reduce maintenance times. This paper presents a Bayesian optimized discriminant analysis model to classify and monitor the tool condition into three user-defined classes. The data is collected using an in-house designed and developed Data Acquisition (DAQ) module set up on a Vertical Machining Center (VMC). The hyperparameter tuning has been incorporated using Bayesian optimization search, and the parameter which gives the best model was found out to be ‘Linear’, achieving an accuracy of 93.3%. This work confirms the feasibility of machine learning techniques like DA in the field of TCM and using Bayesian optimization algorithms to fine-tune the model, making it industry-ready.
TL;DR: A computational strategy of spatiotemporal compressive sensing of the full-field Lagrangian displacement response from the video of the vibrating structure with unknown geometric properties and boundary conditions is suggested.
TL;DR: In this article, an intelligent feature selection and classification method for fault diagnosis of WGs under different working conditions was introduced. But the novelty of the study lies in the selection of feature sources and different loading and speed conditions for condition monitoring studies of WG experimentally.
TL;DR: In this paper , a novel condition monitoring and fault isolation system for wind turbines based on supervisory control and data acquisition (SCADA) data has been developed, where a covariate-adjusted preprocessing procedure is proposed to account for the various working conditions of the wind turbine.
Abstract: Condition monitoring of the wind turbine based on supervisory control and data acquisition (SCADA) data has attracted much attention in recent years. Nevertheless, there are some inherent challenges in SCADA data analysis, including the low sampling rate, time-varying working conditions of the wind turbine, and a lack of historical fault data. To solve these problems, this article develops a novel condition monitoring and fault isolation system. First, a covariate-adjusted preprocessing procedure is proposed to account for the various working conditions of the wind turbine. Next, we construct a global monitoring statistic based on all temperature variables contained in the SCADA data, with a view to monitoring the overall health status of the wind turbine. If an alarm is raised, we isolate the fault through a variable selection method without relying on expert knowledge or historical fault data. Simulation and real cases are provided to demonstrate the effectiveness of this system.
TL;DR: In this paper , the authors developed a vibration-based health indicator to monitor the gear surface degradation induced by gear wear progression, which can be used to reduce maintenance costs and minimize gearbox failures in wind turbines.
TL;DR: In this paper , a self-supervised health representation learning method is proposed to address the problems of volatile operating conditions and the dependence on the assumption that healthy and unhealthy measurements can be naturally separated after the training stage.
TL;DR: In this paper, a machine learning model-based data-driven approach is presented to accurately evaluate the performance of the turbines and diagnose the faults, which is based on Long Short Term Memory (LSTM) incorporating a statistical tool named Kullback-Leibler divergence (KLD).
TL;DR: In this paper, Hjorth's parameters are applied to vibration signals for fault detection in ball bearings and two open-access datasets are used: the NASA bearing dataset of the University of Cincinnati and the Polytechnic of Turin rolling bearing dataset.
TL;DR: In this paper , a machine learning model-based data-driven approach is presented to accurately evaluate the performance of the turbines and diagnose the faults, which is based on Long Short Term Memory (LSTM) incorporating a statistical tool named Kullback-Leibler divergence (KLD).
TL;DR: A compressive sensing-based missing-data-tolerant fault detection method for remote condition monitoring of wind turbines by using the data of a generator current signal collected from each wind turbine remotely while considering different data loss rates.
Abstract: Compared with traditional onsite wind turbine condition monitoring systems (CMSs), the remote CMSs can use better computational resources to process data with more advanced algorithms and, thus, can provide more advanced condition monitoring capabilities, but may suffer from a data loss problem, especially when wireless data transmission is used. To solve this problem, this article proposes a compressive sensing-based missing-data-tolerant fault detection method for remote condition monitoring of wind turbines. First, the condition monitoring signals collected from wind turbines are conditioned to increase their sparsity. Then, a compressive-sensing-based sampling algorithm is designed to sample the conditioned signals. The resulting data samples, called measurements of the conditioned signals are transmitted wirelessly during which some data samples are possibly lost. At the data receiving end, the conditioned signals are reconstructed from the received data samples, which might be incomplete, via a compressive-sensing-based signal reconstruction algorithm. Finally, spectrum analysis is performed on the reconstructed signals for wind turbine fault detection via fault characteristic frequency identification. The proposed method is validated for bearing fault detection of a Skystream 3.7 wind turbine and an Air Breeze wind turbine by using the data of a generator current signal collected from each wind turbine remotely while considering different data loss rates.
TL;DR: In this article , the use of acoustic emission to predict fault detection in rolling mill roller bearings in relation to the gradual rise in defect size was defined, and a bearing test rig was designed and developed to investigate various defects in rolling element bearings in a real-world environment.
TL;DR: A plausible theoretical perspective inspired from neuroscience is proposed for signal representation of deep learning framework to model machine perception in structural health monitoring (SHM), especially because SHM typically involves multiple sensory input from different sensing locations.
TL;DR: In this paper , a hybrid CNN-MLP model-based diagnostic method was proposed to detect and localize bearing defects using acceleration data from a wireless acceleration sensor which is mounted on a rotating shaft of the machine.
TL;DR: In this paper , a new sensor fault diagnosis method for gas leakage monitoring has been proposed derived from the Naive Bayes Classifier (NBC) and Probabilistic Neural Network (PNN).
TL;DR: In this paper , the authors provide a state-of-the-art review for the various condition monitoring technologies used for oil-immersed power transformers and present a concept of measurements and analysis of the results along with the future trend of condition monitoring techniques.
Abstract: A power transformer is one of the most critical and expensive assets in electric power systems. Failure of a power transformer would not only result in a downtime to the entire transmission and distribution networks but may also cause personnel and environmental hazards due to oil leak and fire. Hence, to enhance a transformer’s reliability and extend its lifespan, a cost-effective and reliable condition monitoring technique should be adopted from day one of its installation. This will help detect incipient faults, extend a transformer’s operational life, and avoid potential consequences. With the global trend to establish digital substation automation systems, transformer online condition monitoring has been given much attention by utilities and researchers alike. Several online and offline condition monitoring techniques have been recently proposed for oil-immersed power transformers. This paper is aimed at providing a state-of-the-art review for the various condition monitoring technologies used for oil-immersed power transformers. Concept of measurements and analysis of the results along with the future trend of condition monitoring techniques are presented.