Condition Monitoring Scheme Research Articles

Integrated condition monitoring scheme for bearing fault diagnosis of a wind turbine gearbox

Rolling element bearing faults of a laboratory scale wind turbine gearbox operating under nonstationary loads have been diagnosed using condition monitoring (CM) techniques such as vibration analysis, acoustic analysis, and lubrication oil analysis. Two local bearing faults, namely, bearing inner race fault and bearing outer fault are seeded in the gearbox. The raw data from these techniques are decomposed and wavelet approximation coefficients of level four (a4) are extracted using discrete wavelet transform (DWT). A plethora of statistical features is computed from the wavelet approximation coefficients and the most significant features are being identified by implementing the decision tree algorithm. The classification efficiencies of each of these CM techniques are compared by using the support-vector machine algorithm. Furthermore, an integrated CM scheme is developed by combining the individual CM techniques and the fault diagnosing ability of the integrated CM scheme is compared with the individual CM techniques. A principal component analysis-based approach is used as a feature classification algorithm and an input feature matrix is formed by combining the significant features extracted from vibration, acoustic, and lubrication oil analysis. It has been observed that the integrated CM scheme has provided better classification interpretations than the single CM techniques and it can be extended for real time fault diagnosis of a wind turbine gearbox.

Review on model-based methods for on-board condition monitoring in railway vehicle dynamics

This article performs an extensive review on condition monitoring techniques for rail vehicle dynamics. In particular, the review focuses on applications of model-based approaches for on-board condition monitoring systems. The article covers condition monitoring schemes, fault detection strategies as well as theoretical aspects of different techniques. Case studies and experimental applications are also summarized. All the mentioned issues are discussed with the goal of providing a detailed overview on condition monitoring in railway vehicle dynamics.

Five-axis machine tools accuracy condition monitoring based on volumetric errors and vector similarity measures

Abstract The accuracy of a machine tool affects the geometry and dimensions of machined parts. A machine tool accuracy condition monitoring scheme using volumetric errors (VEs), vector similarity measures (VSMs) and exponentially weighted moving average (EWMA) control chart is proposed in this research. The usefulness of this scheme is tested with simulated machine error data as well as real machine tool tests using NC induced geometric error changes and a real C-axis encoder fault. Both sudden and gradual changes were considered for the simulated faults. The results show that VE is a meaningful quantity for the monitoring of the machine tool accuracy condition. The proposed VSMs work well in VEs feature extraction. Amongst the studied VSMs, the module of the vectorial difference of two consecutive VE vectors (Dist) and the angle between those vectors (Cos2) are more stable and perform better for monitoring faults with sudden and gradual changes than the remaining VSMs in real VE data processing. Finally, this research provides guidelines for the use of VEs as well as a VE-based monitoring strategy for monitoring machine tool accuracy condition.

Perspectives on condition monitoring techniques of wind turbines

The ever-increasing development of wind power plants has raised awareness that an appropriate condition monitoring system is required to achieve high reliability of wind turbines. In order to develop an efficient, accurate and reliable condition monitoring system, the operations of wind turbines need to be fully understood. This article focuses on the online condition monitoring of electrical, mechanical and structural components of a wind turbine to diminish downtime due to maintenance. Failure mechanisms of the most vulnerable parts of wind turbines and their root causes are discussed. State-of-the-art condition monitoring methods of the different parts of wind turbine such as generators, power converters, DC-links, bearings, gearboxes, brake systems and tower structure are reviewed. This article addresses the existing problems in some areas of condition monitoring systems and provides a novel method to overcome these problems. In this article, a comparison between existing condition monitoring techniques is carried out and recommendations on appropriate methods are provided. In the analysis of the technical literature, it is noted that the effect of wind speed variation is not considered for traditional condition monitoring schemes.

Designand application of data aquistion interface circuit

A commitment to condition monitoring involves the operators of plant in the conduct of a range of activities. These activities may be complicated in nature and indeed may often be performed automatically under computer control. They can, however, always be down into a relatively small number of easily identifiable functional tasks. This makes it much easier to identify the common elements of machine condition monitoring schemes. A proposed interface circuit design and application will be further explain in this paper, the implemented monitoring unit circuit also illustrated, see appendix A. Two scenarios presented in this paper, first ten turns assume to be shorted, and in the second thirty turns shorted to show the difference in the amplitude of frequencies at each case. This paper present. An improvement in three-phase squirrel-cage induction motor stator inter-turn fault detection and diagnosis based on a neural network approach is presented.

EEMD-Based Steady-State Indexes and Their Applications to Condition Monitoring and Fault Diagnosis of Railway Axle Bearings.

Railway axle bearings are one of the most important components used in vehicles and their failures probably result in unexpected accidents and economic losses. To realize a condition monitoring and fault diagnosis scheme of railway axle bearings, three dimensionless steadiness indexes in a time domain, a frequency domain, and a shape domain are respectively proposed to measure the steady states of bearing vibration signals. Firstly, vibration data collected from some designed experiments are pre-processed by using ensemble empirical mode decomposition (EEMD). Then, the coefficient of variation is introduced to construct two steady-state indexes from pre-processed vibration data in a time domain and a frequency domain, respectively. A shape function is used to construct a steady-state index in a shape domain. At last, to distinguish normal and abnormal bearing health states, some guideline thresholds are proposed. Further, to identify axle bearings with outer race defects, a pin roller defect, a cage defect, and coupling defects, the boundaries of all steadiness indexes are experimentally established. Experimental results showed that the proposed condition monitoring and fault diagnosis scheme is effective in identifying different bearing health conditions.

Condition monitoring of a steam turbine generator using wavelet spectrum based control chart

Condition-based maintenance (CBM) is designed to take maintenance actions only when there is an imminent evidence of failure for a monitoring system. The parameters indicating health status of the system are continuously monitored in CBM. This article proposes a condition monitoring scheme based on energy profiles generated from wavelet spectrum analysis. The energy of time series is represented by a wavelet spectrum in scale representations of signals. After deriving wavelet spectrums using a discrete wavelet transform at pre-specified windows, we aim to monitor the system based on multivariate T2 chart for the parameters in the linear energy profiles. The monitoring scheme is applied to temperature signals measured from a steam turbine generator. The proposed T2 chart based on the energy profiles shows a potential in early detecting the abnormality of a monitoring system which is not clearly detectable in original time scales.

Diagnosis methodology for identifying gearbox wear based on statistical time feature reduction

Strategies for condition monitoring are relevant to improve the operation safety and to ensure the efficiency of all the equipment used in industrial applications. The feature selection and feature extraction are suitable processing stages considered in many condition monitoring schemes to obtain high performance. Aiming to address this issue, this work proposes a new diagnosis methodology based on a multi-stage feature reduction approach for identifying different levels of uniform wear in a gearbox. The proposed multi-stage feature reduction approach involves a feature selection and a feature extraction ensuring the proper application of a high-performance signal processing over a set of acquired measurements of vibration. The methodology is performed successively; first, the acquired vibration signals are characterized by calculating a set of statistical time-based features. Second, a feature selection is done by performing an analysis of the Fisher score. Third, a feature extraction is realized by means of the linear discriminant analysis technique. Finally, fourth, the diagnosis of the considered faults is done by means of a fuzzy-based classifier. The effectiveness and performance of the proposed diagnosis methodology are evaluated by considering a complete data set of experimental test, making the proposed methodology suitable to be applied in industrial applications with power transmission systems.

Multifault Diagnosis Method Applied to an Electric Machine Based on High-Dimensional Feature Reduction

Condition monitoring schemes are essential for increasing the reliability and ensuring the equipment efficiency in industrial processes. The feature extraction and dimensionality reduction are useful preprocessing steps to obtain high performance in condition monitoring schemes. To address this issue, this work presents a novel diagnosis methodology based on high-dimensional feature reduction applied to detect multiple faults in an induction motor linked to a kinematic chain. The proposed methodology involves a hybrid feature reduction that ensures a good processing of the acquired vibration signals. The method is performed sequentially. First, signal decomposition is carried out by means of empirical mode decomposition. Second, statistical-time-based features are estimated from the resulting decompositions. Third, a feature optimization is performed to preserve the data variance by a genetic algorithm in conjunction with the principal component analysis. Fourth, a feature selection is done by means of Fisher score analysis. Fifth, a feature extraction is performed through linear discriminant analysis. And, finally, sixth, the different considered faults are diagnosed by a Neural Network-based classifier. The performance and the effectiveness of the proposed diagnosis methodology is validated experimentally and compared with classical feature reduction strategies, making the proposed methodology suitable for industry applications.

Mobile device-based shaft speed estimation

Abstract One of the fundamental parameters that needs to be fed into a rotating machinery condition monitoring system is the rotational speed of shaft operating in the system. This parameter is essential in order to derive other characteristic frequencies that may contain some valuable diagnostic information. There are many dedicated hardware elements used in determining the rotational speed of elements however the installation of them usually translates to additional cost of the entire condition monitoring scheme, especially if retrofitting is concerned, and may not always be financially viable due to potentially long payback period on the investment, as in the case of e.g. LV induction motors. This text investigates the possibility of assessing the rotational speed of a shaft based solely on the capabilities of a standard, off-the-shelf mobile phone. The results presented in this text seem to indicate that with an appropriate signal processing approach, it is possible to ascertain the speed of the rotating element using a tool available nowadays to virtually all service technicians.

Bayesian Identification of Hidden Markov Models and Their Use for Condition-Based Monitoring

In this paper, we propose a Bayesian estimation scheme for hidden Markov model (HMM) parameters, as well as a method for monitoring systems whose degradation processes are modeled using HMMs identified using this novel estimation approach. The Bayesian estimation naturally yields information about model parametric uncertainties via posterior distributions of HMM parameters emanating from the estimation procedure. Numerous simulations implementing the newly proposed method for estimation of HMM parameters were conducted demonstrating its capability to identify several types of HMMs, including the commonly encountered ergodic and homogeneous HMM, as well as less commonly studied nonergodic or/and nonhomogeneous HMM. In addition, a novel condition monitoring scheme based on uncertain HMMs of the degradation process is proposed and demonstrated on a large dataset obtained from a semiconductor-manufacturing facility. A small portion of the data was used to build operating mode specific HMMs of machine degradation via the newly proposed Bayesian estimation, while the remainder of the data was used for condition monitoring based on the uncertain degradation HMMs yielded by the novel Bayesian estimation method. Comparison with a traditional sensory signature-based statistical monitoring method showed that the newly proposed approach significantly outperforms the traditional method in terms of its detection capabilities and false alarm ratios.

Failure prediction and reliability analysis of ferrocement composite structures by incorporating machine learning into acoustic emission monitoring technique

This paper introduces suitable features and methods to define hazard rate function by acoustic emission (AE) parametric analysis to develop robust damage statement index and reliability analysis. AE signal energy was first examined to find out the relation between damage progress and AE signal energy so that a damage index based on AE signal energy could be proposed to quantify progressive damage imposed to ferrocement composite slabs. Moreover, by using AE signal strength, historic index could be computed and utilized to develop a modified hazard rate function through integration of bathtub curve and Weibull function. Furthermore, to provide a practical scheme for real condition monitoring, support vector regression was utilized to produce a robust tools for failure prediction considering uncertainties exist in real structures.

Induction Motor Stator Fault Detection by a Condition Monitoring Scheme Based on Parameter Estimation Algorithms

This article presents a simple, low-cost, and effective method for the early diagnosis of stator short-circuit faults. The approach relies on the combination of an induction motor mathematical model and parameter estimation algorithm. The kernel of the method is the efficient search for the characteristic parameters that indicate stator short-circuit faults. However, the non-linearity of a machine model may imply multiple local minima of an objective function implemented in the estimation algorithm. Taking this into consideration, the suitability of two industry-proven optimization algorithms (pattern search algorithm and genetic algorithm) as applied in the proposed condition monitoring method was investigated. Experimental results show that the proposed diagnosis method is capable of detecting stator short-circuit faults and estimating level and location of faults. The study also indicates that the proposed method is robust to motor parameters offset and unbalanced voltage supply. Application of the pattern search algorithm is suitable for a continuous monitoring system, where the previous result can be used as starting point of the new search. The genetic algorithm requires longer computation time and is suitable for the offline diagnostic system. It is not sensitive to the starting point, and achieving global solution is guaranteed.

An efficient recursive least square-based condition monitoring approach for a rail vehicle suspension system

ABSTRACTA model-based condition monitoring strategy for the railway vehicle suspension is proposed in this paper. This approach is based on recursive least square (RLS) algorithm focusing on the deterministic ‘input–output’ model. RLS has Kalman filtering feature and is able to identify the unknown parameters from a noisy dynamic system by memorising the correlation properties of variables. The identification of suspension parameter is achieved by machine learning of the relationship between excitation and response in a vehicle dynamic system. A fault detection method for the vertical primary suspension is illustrated as an instance of this condition monitoring scheme. Simulation results from the rail vehicle dynamics software ‘ADTreS’ are utilised as ‘virtual measurements’ considering a trailer car of Italian ETR500 high-speed train. The field test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real application. Results of the parameter identification performed indicate that estimated suspension parameters are consistent or approximate with the reference values. These results provide the supporting evidence that this fault diagnosis technique is capable of paving the way for the future vehicle condition monitoring system.

Enhanced Industrial Machinery Condition Monitoring Methodology Based on Novelty Detection and Multi-Modal Analysis

This paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the demanding requirements of the future industrial processes monitoring systems. First, the method is based on the temporal segmentation of the available physical magnitudes, and the estimation of a set of time-based statistical features. Then, a double feature reduction stage based on principal component analysis and linear discriminant analysis is applied in order to optimize the classification and novelty detection performances. The posterior combination of a feed-forward neural network and one-class support vector machine allows the proper interpretation of known and unknown operating conditions. The effectiveness of this novel condition monitoring scheme has been verified by experimental results obtained from an automotive industry machine.

Robust condition monitoring of rolling element bearings using de-noising and envelope analysis with signal decomposition techniques

This study presents a robust condition monitoring methodology for rolling element bearings that employs a novel empirical mode decomposition (EMD)-based method to eliminate high-level noise from an acoustic emission (AE) signal and a discrete wavelet packet transform (DWPT)-based envelope analysis technique to effectively search for symptoms of defective bearings. First, the proposed EMD-based de-noising scheme enhances the signal-to-noise ratio by using a Naïve Bayes classifier that partitions intrinsic mode functions (IMFs) into noise-dominant and noise-free categories, employing a soft-thresholding-based noise reduction technique for the noise-dominant IMFs, finally obtaining a de-noised acoustic emission (AE) signal via the reconstruction process using both de-noised IMFs and noise-free IMFs. The de-noised AE signal is then decomposed into a set of uniformly spaced sub-bands using three-level DWPT, and the most informative sub-band is determined for early detection of bearing failures. The performance of the proposed condition monitoring scheme is compared with the performance of conventional methods in terms of mean-peak ratio (MPR), which is a metric used to evaluate the degree of defectiveness of the bearings. The experimental results show that the proposed method outperforms the conventional schemes by achieving up to 23.48% higher MPR values, even in a very noisy environment.

Iterative Condition Monitoring and Fault Diagnosis Scheme of Electric Motor for Harsh Industrial Application

This paper presents a robust diagnosis technique by iteratively analyzing the pattern of multiple fault signatures in a motor current signal. It is mathematically and experimentally proved that the proposed diagnosis algorithm provides highly accurate monitoring performance while minimizing both false detection and miss detection rate under high noise and nonlinear machine operating condition. These results are verified on a digital-signal-processor-based motor drive system where motor control and fault diagnosis are performed in real time.

Deterioration Monitoring of DC-Link Capacitors in AC Machine Drives by Current Injection

This letter proposes a novel condition monitoring scheme of dc-link capacitors in PWM inverter-fed induction machine drives with front-end diode rectifiers, which is based on the online capacitance estimation scheme. While the motor is operating in the regenerative mode for the estimation process, a regulated ac component is injected into the stator winding, which causes a dc-link voltage ripple at the same frequency. From the ac components of the dc-link voltage and current, the capacitance is estimated with a recursive least squares algorithm. With this method, experimental results have shown that the estimation error of the capacitance is less than 1%, from which the deterioration condition of the capacitors can be diagnosed reliably.

Review and Application of Model and Spectral Analysis Based Fault Detection and Isolation Scheme in Actuators and Sensors

For condition monitoring of machineries and systems conventional method such as hardware or sensor based error checking scheme were in use. As the automated systems are becoming complex, recently most of the condition-monitoring schemes have been applying sophisticated analytical tools and methods to achieve improved performance. The objective of this paper is to demonstrate model based Fault Detection and Isolation (FDI) schemes for mechatronic systems and devices. First we have reviewed FDI approaches and implementation schemes. Then, we have developed two frameworks: model and spectral signature based for the implementation of FDI schemes. The model based feature estimation and spectral analysis based multiresolution methods are implemented in exemplar devices such as actuators and sensors used in mechatronic systems. Based on the frameworks, the diagnostics and isolation algorithms were developed using MATLAB code. The algorithms are capable of detecting and isolating faults within the systems. The study is comprehensive and the implementation scenarios can be extendible to many types of systems and devices used in the mechatronic domain.

Nonlinear system identification for model-based condition monitoring of wind turbines

This paper proposes a data driven model-based condition monitoring scheme that is applied to wind turbines. The scheme is based upon a non-linear data-based modelling approach in which the model parameters vary as functions of the system variables. The model structure and parameters are identified directly from the input and output data of the process. The proposed method is demonstrated with data obtained from a simulation of a grid-connected wind turbine where it is used to detect grid and power electronic faults. The method is evaluated further with SCADA data obtained from an operational wind farm where it is employed to identify gearbox and generator faults. In contrast to artificial intelligence methods, such as artificial neural network-based models, the method employed in this paper provides a parametrically efficient representation of non-linear processes. Consequently, it is relatively straightforward to implement the proposed model-based method on-line using a field-programmable gate array.