Condition Monitoring Of Components Research Articles

Electrostatic Signal Self-Adaptive Denoising Method Combined with CEEMDAN and Wavelet Threshold

A novel low-pass filtering self-adaptive (LPFA) denoising method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a wavelet threshold (WT) strategy is proposed to solve the problem of the aero-engine gas-path electrostatic signal noise, which challenges the gas-path component condition monitoring and feature extraction techniques. Firstly, the integration of CEEMDAN addresses modal aliasing and intermittent signal challenges, while the proposed low-pass filtering method autonomously selects valuable signal components. Additionally, the application of the WT in the unselected components enhances the extraction of useful information, presenting a unique and advanced approach to electrostatic signal denoising. Moreover, the proposed method is applied to simulated signals with different input signal-to-noise ratios and experimental fault electrostatic signals of a micro-turbojet engine. The comparison with several traditional approaches in a denoising test for the simulated signals and experimental signals reveals that the proposed method performs better in extracting the effective components of the signal and eliminating noise.

An Estimation Method of High-Order LC Circuits in Power Electronic Converters

This paper proposes a parameter estimation method for high-order LC circuits conducted during the power-OFF of power electronic converters. It is based on a battery-powered compact signal injection unit and a circuit modeling method to estimate the LC circuit parameters. The proposed method aims for LC component condition monitoring in applications with routine-based maintenance or frequent ON/OFF operation (e.g., wind power, photovoltaic, traction systems). Compared to LCR meters and impedance analyzers, the proposed method has a significantly lower implementation cost and does not require disassembly of components. Moreover, it is a converter-level method in which the signal injection unit only needs to connect the converter inputs or outputs. Compared to power-ON LC parameter estimation methods, the proposed one is non-invasive without adding any hardware or software algorithm that may introduce new risks to converter operation. This method can handle the converters with high-order LC structures (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{nd}$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4^{th}$</tex-math></inline-formula> -order) in converter power-OFF state, which has not been achieved by existing power-OFF solutions. Therefore, the proposed method is promising for the specific applications of interest mentioned above. The parameter estimation algorithm and signal injection unit design are presented. The experimental results indicate that the estimation errors of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{nd}$</tex-math></inline-formula> -order equivalent circuit, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3^{rd}$</tex-math></inline-formula> -order equivalent circuit, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4^{th}$</tex-math></inline-formula> -order equivalent circuit are lower than 1.6%, 2.5%, and 3%, respectively. The estimated LC parameters can be further used as inputs for operation optimization or condition monitoring of the converters.

La(Ca)CrO3-Filled SiCN Precursor Thin Film Temperature Sensor Capable to Measure up to 1100 °C High Temperature.

Thin-film sensors are regarded as advanced technologies for in situ condition monitoring of components operating in harsh environments, such as aerospace engines. Nevertheless, these sensors encounter challenges due to the high-temperature oxidation of materials and intricate manufacturing processes. This paper presents a simple method to fabricate high temperature-resistant oxidized SiCN precursor and La(Ca)CrO3 composite thin film temperature sensors by screen printing and air annealing. The developed sensor demonstrates a broad temperature response ranging from 200 °C to 1100 °C with negative temperature coefficients (NTC). It exhibits exceptional resistance to high-temperature oxidation and maintains performance stability. Notably, the sensor's resistance changes by 3% after exposure to an 1100 °C air environment for 1 h. This oxidation resistance improvement surpasses the currently reported SiCN precursor thin-film sensors. Additionally, the sensor's temperature coefficient of resistance (TCR) can reach up to -7900 ppm/°C at 200 °C. This strategy is expected to be used for other high-temperature thin-film sensors such as strain gauges, heat flux sensors, and thermocouples. There is great potential for applications in high-temperature field monitoring.

Development of fault diagnosis for nuclear power plant using deep learning and infrared sensor equipped UAV

Fault component detection is necessary for safety and maintenance in large-scale industrial fields including nuclear power plants. Therefore, this study proposes a method for diagnosing a power plant composed of numerous components based on deep learning using a UAV with an IR sensor and a camera. The proposed method could diagnose the components and recognize the fault component in real time. In this study, a thermal–hydraulic integral effect test facility, which is a scaled-down nuclear power plant, is utilized considering the nuclear power plant. The database for the application of deep learning was performed by combining an IR intensity map and general image to enhance the performance of component classification and fault detection. Deep learning was applied using object detection and classification methods based on convolutional neural networks (CNNs) that are effective for image processing. As a result, this technology can diagnose the multi-component by a single measurement instrument. The optimal performance of component classification and fault detection was 55.9 ms per 16 batches, demonstrating a mean average precision (mAP) of 0.9913. This technology could be applied to various industries as a comprehensive component condition monitoring method for operating efficiency and safety.

Data-Driven Sliding Bearing Temperature Model for Condition Monitoring in Internal Combustion Engines

Condition monitoring of components in internal combustion engines is an essential tool for increasing engine durability and avoiding critical engine operation. If lubrication at the crankshaft main bearings is insufficient, metal-to-metal contacts become likely and thus wear can occur. Bearing temperature measurements with thermocouples serve as a reliable, fast responding, individual bearing-oriented method that is comparatively simple to apply. In combination with a corresponding reference model, such measurements could serve to monitor the bearing condition. Based on experimental data from an MAN D2676 LF51 heavy-duty diesel engine, the derivation of a data-driven model for the crankshaft main bearing temperatures under steady-state engine operation is discussed. A total of 313 temperature measurements per bearing are available for this task. Readily accessible engine operating data that represent the corresponding engine operating points serve as model inputs. Different machine learning methods are thoroughly tested in terms of their prediction error with the help of a repeated nested cross-validation. The methods include different linear regression approaches (i.e., with and without lasso regularization), gradient boosting regression and support vector regression. As the results show, support vector regression is best suited for the problem. In the final evaluation on unseen test data, this method yields a prediction error of less than 0.4 °C (root mean squared error). Considering the temperature range from approximately 76 °C to 112 °C, the results demonstrate that it is possible to reliably predict the bearing temperatures with the chosen approach. Therefore, the combination of a data-driven bearing temperature model and thermocouple-based temperature measurements forms a powerful tool for monitoring the condition of sliding bearings in internal combustion engines.

Analysis and evaluation of the irradiated dimensional and volume change behavior of nuclear graphites

Literature on the changes in the specimen dimensions and volume after high energy neutron irradiation of more than a dozen graphites were examined. Some of these data have been previously used to design graphite components in gas cooled reactors. The dimensional change behavior of a graphite is rather unique and depends on both the irradiation temperature and accumulated irradiation dose. The characteristics of the dimensional and volume change behavior of several graphites were analyzed and evaluated from engineering design consideration of graphite components. Specifically, six parameters were derived from the dimensional and volume change behavior. These parameters can serve as “figures-of-merit” (FOM) to enable engineering judgement on the irradiation-induced dimensional and volume change behavior of nuclear graphites, and are referred to as “derived figure-of-merit (DFOM).” The DFOMs also provide engineering bases to examine the opportunity to select several competing graphites for fulfilling the design and operation of components in reactor service. The utility of these DFOMs can be realized potentially in designing inservice inspection and component condition monitoring (CCM) protocols that are effective and efficient both technically and economically. The DFOMs, in combination, also can guide the reactor operator to arrive at optimum component replacement schedule for graphite components, which ensure maintaining graphite components functional requirements and related reactor safety issues.

Comprehensive design analysis of a 3D printed sensor for readiness assessment applications

PurposeThis paper aims to develop a comprehensive model of a magnetic sensor array that will be operational for a multitude of electric components in continuous and nonintrusive condition monitoring (CM) or in readiness assessment (RA) applications.Design/methodology/approachA universal nonintrusive model of a flexible antenna array is introduced to monitor and identify failures in electric machine drives. An adjustable sensor is designed to serve as a RA for a vast range of electrical elements in a typical power system by capturing the low-frequency radiated magnetic fields.FindingsThe optimal placement of the most sensitive radiated fields from several components has been discovered in this case study, enabling the detection of healthy current flow throughout. Thereafter, the short-circuit investigation, representing faulty situations, is implemented and compared with healthy cases.Practical implicationsThis sensing technique can be used for nonintrusive CM of components that are out of reach and cannot have the sensor to be held around it such as components in offshore winds, wind energy generation and power and chemical plants.Originality/valueThe results are provided for three commonly used machines with a single sensor array with numerous settings. The three dimensional (3 D) finite element analysis is applied in the structuring of the sensor, detection of the optimum location and recognition of faults in the machines. Finally, based on the setup design, 3 D printing is used for the construction of the sensor array. Thus, the sensor array with fault detection avoids major component failures and increases system reliability/resiliency.

Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model

Abstract Reconstruction model is a powerful method for component condition monitoring and fault detection by considering the model prediction residuals. In this article, a new signal reconstruction modeling technique is proposed using support vector regression. Multiple indicators are calculated to recognize slight shift from normal condition, and detect the fault at an early stage. Input variables are selected based on correlation analysis and failure mode analysis. A sliding-time-window technique is employed to incorporate temporal information inherent in time-series data. Residuals between the observed signal and the reconstruction signal are utilized to indicate whether the desired quantity is different from its normal operation condition or not. Three statistical indicators (Deviation Index, Volatility Index and Significance Index) are defined to quantify the deviation level from normal condition to abnormal condition. Health index (HI) of a specific fault is derived from responsive statistical indicators, and the integral health index (integral-HI) of an entire component is composed of all individual health index. An experiment of real-life wind turbine high temperature fault detection scheme is studied. Results show that the proposed approach demonstrates improved performance in detecting wind turbine faults, and controlling false and missed alarms.

Improvement of Defect Detectability in Machine Tools Using Sensor-based Condition Monitoring Applications

The aim of this paper is to present a reliable methodology for condition monitoring of components of high performance centerless grinding machines. This enables the detection and localization of the defects on the ball screw. The fault detection is realized using a self-implemented classification algorithm and other pattern recognition algorithms. The diagnosis is based on acceleration and AE measurement data performed on an axis test rig using various damaged ball screws at different operating parameters. Moreover, the structure of the pattern recognition process will be introduced, this includes the signal pre-processing, the selection of the most suitable features for this specific application using MATLAB®. Finally, the evaluation of the developed solution will be showed. The actions allowing improving the accuracy of measurement data and the effectiveness of the processing algorithms, based on MATLAB® applications, are described throughout the paper. Satisfactory classification results will be obtained and discussed.

Generalized Transmissibility Damage Indicator With Application to Wind Turbine Component Condition Monitoring

Frequency methods such as frequency spectrum analysis, frequency spike detection, demodulation, envelope spectrum method have been widely used for condition monitoring of engineering structural systems. Different from the conventional frequency methods, the transmissibility function (TF) represents the relationship between different system output responses such as, e.g., vibration and acoustic emission sensor measurements. This paper introduces a simple and effective generalized transmissibility damage indicator (GTDI) for TF-based condition monitoring. Unlike the conventional transmissibility damage indicator, the new GTDI can improve the detection sensitivity, reduces noise effects and avoid dynamic loadings effects. This is achieved by combining multiple groups of data to obtain more accurate transmissibility analysis, exploiting all the available TFs, and using multiple references. This has two advantages. First, it does not require any other priori knowledge about the system responses. Therefore, the method can be used for the condition monitoring of a wide range of components or systems. Furthermore, the method can be easily implemented using fast Fourier transform or power spectra density methods and therefore is computationally efficient. These make the method very suitable for implementing online real-time condition monitoring. The method is investigated by simulation studies and then applied to analyze the vibration data of the main bearing of operating wind turbines, producing very promising results.

Railway track component condition monitoring using optical fibre Bragg grating sensors

The use of optical fibre Bragg grating (FBG) strain sensors to monitor the condition of safety critical rail components is investigated. Fishplates, switchblades and stretcher bars on the Stagecoach Supertram tramway in Sheffield in the UK have been instrumented with arrays of FBG sensors. The dynamic strain signatures induced by the passage of a tram over the instrumented components have been analysed to identify features indicative of changes in the condition of the components.

Effective and accurate approaches for wind turbine gearbox condition monitoring

ABSTRACTThis paper presents effective and accurate approaches in vibration‐based wind turbine drivetrain component condition monitoring. Detailed spectral analysis and acceleration enveloping techniques were used to effectively extract the gear and bearing damage features. Synchronous analysis was used to accurately detect specific damage features during constantly varying operational conditions. A typical wind turbine gearbox amplifies shaft speed two orders of magnitude from the rotor to the generator. To account for all necessary vibration signatures, synchronous sampling must be carried out for multiple revolutions and at a relatively high rate. The synchronous sampling used in this paper was carried out in the digital domain after both the keyphasor and the vibration signals were digitized at high sampling rate and high sampling resolution analog‐to‐digital conversion. Sometimes, the shaft speed is provided in a speed time history format, as in the case of the National Renewable Energy Laboratory (NREL) Round Robin project. To carry out synchronous sampling using the speed time history, a unique synthesized synchronous sampling technique was adopted. The approach presented in this paper was realized using MATLAB (MathWorks, Natick, MA) codes and then validated with a wind turbine field case. It was also applied to the NREL wind turbine drivetrain condition monitoring Round Robin project. The identified damage results using the techniques discussed were compared with post‐test inspection results with good correlation. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Evaluation of Component Reliability Using Data Derived from Infrared Thermography

Condition monitoring of components is commonly required to evaluate integrity, manage maintenance and predict impending failure. The condition of many electrical and mechanical components is revealed by their thermal signature which can be reliably monitored by infrared camera. Thermal data obtained optically has the advantage of being non-contact so that moving mechanical parts and high voltage electrical components, including those located in hazardous environments or with difficult access, can be readily monitored. This paper proposes that data obtained by such methods is processed within a model in order to identify the imminent failure of a component or to estimate the probability that a component may exceed its operating tolerances at any given time within the remainder of its expected life.