Reliable Condition Monitoring Research Articles

Wind turbine condition monitoring by the approach of SCADA data analysis

Wind turbines are being increasingly deployed in remote onshore and offshore areas due to the richer wind resource there and the advantages of mitigating the land use and visual impact issues. However, site accessing difficulties and the shortage of proper transportation and installation vehicles/vessels are challenging the operation and maintenance of the giants erected at these remote sites. In addition to the continual pressure on lowering the cost of energy of wind, condition monitoring is being regarded as one of the best solutions for the maintenance issues and therefore is attracting significant interest today. Much effort has been made in developing wind turbine condition monitoring systems and inventing dedicated condition monitoring technologies. However, the high cost and the various capability limitations of available achievements have delayed their extensive use. A cost-effective and reliable wind turbine condition monitoring technique is still sought for today. The purpose of this paper is to develop such a technique through interpreting the SCADA data collected from wind turbines, which have already been collected but have long been ignored due to lack of appropriate data interpretation tools. The major contributions of this paper include: (1) develop an effective method for processing raw SCADA data; (2) propose an alternative condition monitoring technique based on investigating the correlations among relevant SCADA data; and (3) realise the quantitative assessment of the health condition of a turbine under varying operational conditions. Both laboratory and site verification tests have been conducted. It has been shown that the proposed technique not only has a potential powerful capability in detecting incipient wind turbine blade and drive train faults, but also exhibits an amazing ability in tracing their further deterioration.

Feature extraction, condition monitoring, and fault modeling in semiconductor manufacturing systems

Reliable feature extraction, condition monitoring, and fault modeling are critical to understanding equipment degradation and implementing the proper maintenance decisions in manufacturing processes. Semiconductor manufacturing machines are highly sophisticated systems, consisting of multiple interacting components operating in highly variable operating conditions. This complicates performance monitoring since equipment condition must often be inferred through concurrent interpretation of multiple sensor readings originating from potentially very different subsystems of the tool. This paper presents an integrated approach to feature extraction, condition monitoring, and fault modeling applied to a set of standard built-in sensors on a modern 300-mm technology industrial Plasma Enhanced Chemical Vapor Deposition (PECVD) tool. Linear Discriminant Analysis was utilized to determine the set of dynamic features that are the most sensitive to different tool conditions brought about by chamber cleaning or various faults. Gaussian Mixture Models of the dynamic feature distributions were used to statistically quantify changes of these features as the condition of the tool changed. In addition, four highly detrimental faults were analyzed to demonstrate the fault modeling methodology. Data collected over 8 months from a PECVD tool being operated by a major microelectronics manufacturer was used to verify the methodology. Top sensitive features from various faults observed in this period were examined and physical connections to the chamber condition were interpreted through their behavior.

Research on Condition Monitoring of Bearing Health Using Vibration Data

A reliable condition monitoring system is very useful in a wide range of industries to detect the occurrence of incipient defects so as to prevent machinery performance degradation, malfunction and sudden failure. Among the rotating machinery, many mechanical problems are attributed due to bearing failures. So implementing condition monitoring for bearing is critically needed. Considering that most research for condition monitoring only focus on detecting the existing fault, this paper add degradation tendency prognostics into the condition monitoring process. The kernel of bearing condition monitoring method presented in this paper is related to condition features extraction and remaining useful life prediction. The former is realized by the comprehensive vibration analysis for specific fault frequencies. The latter is achieved by adaptive neuron-fuzzy inference system based on extracted degradation signal. For illustration purpose, a bearing case from NASA data repository is used to validate the feasibility of the proposed method. The result indicates that the performance degradation of bearing can be effectively monitored and the predicted remaining useful life with 5.6% relative error can be the important reference for maintenance decision making.

Wind turbine condition monitoring: technical and commercial challenges

ABSTRACTDeployment of larger scale wind turbine systems, particularly offshore, requires more organized operation and maintenance strategies to ensure systems are safe, profitable and cost‐effective. Among existing maintenance strategies, reliability centred maintenance is regarded as best for offshore wind turbines, delivering corrective and proactive (i.e. preventive and predictive) maintenance techniques enabling wind turbines to achieve high availability and low cost of energy. Reliability centred maintenance analysis may demonstrate that an accurate and reliable condition monitoring system is one method to increase availability and decrease the cost of energy from wind. In recent years, efforts have been made to develop efficient and cost‐effective condition monitoring techniques for wind turbines. A number of commercial wind turbine monitoring systems are available in the market, most based on existing techniques from other rotating machine industries. Other wind turbine condition monitoring reviews have been published but have not addressed the technical and commercial challenges, in particular, reliability and value for money. The purpose of this paper is to fill this gap and present the wind industry with a detailed analysis of the current practical challenges with existing wind turbine condition monitoring technology. Copyright © 2012 John Wiley & Sons, Ltd.

An online technique for condition monitoring the induction generators used in wind and marine turbines

Induction generators have been successfully applied to a variety of industries. However, their operation and maintenance in renewable wind and marine energy industries still face challenges due to harsh environments, limited access to site and relevant reliability issues. Hence, further enhancing their condition monitoring is regarded as one of the essential measures for improving their availability. To date, much effort has been made to monitor induction motors, which can be equally applied to monitoring induction generators. However, the achieved techniques still have constrains in particular when dealing with the condition monitoring problems in wind and marine turbine generators. For example, physical measurements of partial discharge, noise and temperature have been widely applied to monitoring induction machinery. They are simple and cost-effective, but unable to be used for fault diagnosis. The spectral analysis of vibration and stator current signals is also a mature technique popularly used in motor/generator condition monitoring practice. However, it often requires sufficient expertise for data interpretation, and significant pre-knowledge about the machines and their components. In particular in renewable wind and marine industries, the condition monitoring results are usually coupled with load variations, which further increases the difficulty of obtaining a reliable condition monitoring result. In view of these issues, a new condition monitoring technique is developed in this paper dedicated for wind and marine turbine generators. It is simple, informative and less load-dependent thus more reliable to deal with the online motor/generator condition monitoring problems under varying loading conditions. The technique has been verified through both simulated and practical experiments. It has been shown that with the aid of the proposed technique, not only the electrical faults but also the shaft unbalance occurring in the generator become detectable despite the external loading conditions. Moreover, the rotor and stator winding faults can be readily discriminated through observing the variation tendencies of the proposed condition monitoring criteria.

Establishment of Engine Condition Monitoring Alarm System Based on Fuzzy Neural Network

In order to overcome the defect of traditional engine condition monitoring only depending on single parameter, this paper establishes the five-level condition monitoring alert system with fuzzy neural network (FNN) which is good at settling uncertain and complicated problems. Firstly, optimal monitoring parameters are selected from three aspects of ferrography, vibration and performance parameters. With sufficient historical data, limit values of parameters and reliable five-level condition monitoring standards are maintained and established by statistical analysis. Then fuzzy membership functions are applied to transform practical data into fuzzy data. Finally the structure of neural network is designed and trained by sample data. The model is tested with original data and proved to be more effective and reliable to engine condition monitoring.

Fuzzy condition monitoring of recirculation fans and filters

A reliable condition monitoring is needed to predict faults. Pattern recognition technologies are often used for finding patterns in complex systems. Condition monitoring can also benefit from pattern recognition. Many pattern recognition technologies, however, only output the classification of the data sample but do not output any information about classes that are also very similar to the input vector. This paper presents a concept for pattern recognition that output similarity values for decision trees. The concept can be used on top of any normal decision tree algorithms and is independent of the learning algorithm. Performed experiments showed that the concept is reliable and it also works with decision tree forests to increase the classification accuracy.

Passive and Active Thermographic Assessment as a Tool for Condition-Based Performance Monitoring of Photovoltaic Modules

Today, to ensure efficient operation of a photovoltaic (PV) system renders as an undoubtedly major concern. Toward this direction, scope of this work is the assessment of infrared thermography as a PV module’s condition monitoring and, subsequently, performance evaluation method. The idea is based on the fact that any abnormality to the temperature pattern of an under inspection equipment implies a dysfunction case. In particular, specific thermographic measurements were applied to a PV module with known, abnormally low performance. Following the basic processing of the acquired thermal images, the extracted temperature data were contrasted with the PV module’s expected operating temperature. The results validated the presence of a problematic solar cell that refers to a “hot spot” within the tested PV module. This paper concludes with a discussion regarding the advantages, the limitations, and, ultimately, the potentiality of the intended approaches as reliable condition monitoring method through performance evaluation of PV modules.

Bivariate empirical mode decomposition and its contribution to wind turbine condition monitoring

Accessing difficulties and harsh environments require more advanced condition monitoring techniques to ensure the high availability of offshore wind turbines. Empirical mode decomposition (EMD) has been shown to be a promising technique for meeting this need. However, EMD was developed for one-dimensional signals, unable to carry out an information fusion function which is of importance to reach a reliable condition monitoring conclusion. Therefore, bivariate empirical mode decomposition (BEMD) is investigated in this paper to assess whether it could be a better solution for wind turbine condition monitoring. The effectiveness of the proposed technique in detecting machine incipient fault is compared with EMD and a recently developed wavelet-based ‘energy tracking’ technique. Experiments have shown that the proposed BEMD-based technique is more convenient than EMD for processing shaft vibration signals, and more powerful than EMD and wavelet-based techniques in terms of processing the non-stationary and nonlinear wind turbine condition monitoring signals and detecting incipient mechanical and electrical faults.

A Survey of Condition Monitoring and Protection Methods for Medium-Voltage Induction Motors

Medium-voltage (MV) induction motors are widely used in the industry and are essential to industrial processes. The breakdown of these MV motors not only leads to high repair expenses but also causes extraordinary financial losses due to unexpected downtime. To provide reliable condition monitoring and protection for MV motors, this paper presents a comprehensive survey of the existing condition monitoring and protection methods in the following five areas: thermal protection and temperature estimation, stator insulation monitoring and fault detection, bearing fault detection, broken rotor bar/end-ring detection, and air gap eccentricity detection. For each category, the related features of MV motors are discussed; the effectiveness of the existing methods are discussed in terms of their robustness, accuracy, and implementation complexity. Recommendations for the future research in these areas are also presented.

The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery

The monitoring of progressive wear in gears using various non-destructive technologies as well as the use of advanced signal processing techniques upon the acquired recordings to the direction of more effective diagnostic schemes, is the scope of the present work. For this reason multi-hour tests were performed in healthy gears in a single-stage lab scale gearbox until they were seriously damaged. Three on-line monitoring techniques are implemented in the tests. Vibration and acoustic emission recordings in combination with data coming from oil debris monitoring (ODM) of the lubricating oil are utilized in order to assess the condition of the gears. A plethora of parameters/features were extracted from the acquired waveforms via conventional (in time and frequency domain) and non-conventional (wavelet-based) signal processing techniques. Data fusion was accomplished in the level of integration of the most representative among the extracted features from all three measurement technologies in a single data matrix. Principal component analysis (PCA) was utilized to reduce the dimensionality of the data matrix whereas independent component analysis (ICA) was further applied to identify the independent components among the data and correlate them to different damage modes of the gearbox. Finally heuristic rules based on characteristic values of the resulted independent components were set, realizing thus a health monitoring scheme for gearboxes.The integration of vibration, AE and ODM data increases the diagnostic capacity and reliability of the condition monitoring scheme concluding to very interesting results. The present work summarizes the joint efforts of two research groups towards a more reliable condition monitoring of rotating machinery and gearboxes specifically.

Development of a system for monitoring tool condition using acousto-optic emission signal in face turning—an experimental approach

In automated manufacturing systems, one of the most important issues is accurate detection of the tool conditions under given cutting conditions so that worn tools can be identified and replaced in time. In metal cutting as a result of the cutting motion, the surface of workpiece will be influenced by cutting parameters, cutting force, and vibrations, etc. But the effects of vibrations have been paid less attention. In the present paper, an investigation is presented of a tool condition monitoring system, which consists of a fast Fourier transform preprocessor for generating features from an online acousto-optic emission (AOE) signals to develop a database for appropriate decisions. A fast Fourier transform (FFT) can decompose AOE signals into different frequency bands in the time domain. Present work uses a laser Doppler vibrometer for online data acquisition and a high-speed FFT analyser used to process the AOE signals. The generation of the AOE signals directly in the cutting zone makes them very sensitive to changes in the cutting process due to vibrations. AOE techniques is a relatively recent entry into the field of tool condition monitoring. This method has also been widely used in the field of metal cutting to detect process changes like displacement due to vibration and tool wear, etc. In this research work the results obtained from the analysis of acousto-optic emission sensor employs to predict flank wear in turning of AISI 1040 steel of 150 BHN hardness using Carbide insert and HSS tools. The correlation between the tool wear and AOE parameters is analyzed using the experimental study conducted in 16 H.P. all geared lathe. The encouraging results of the work pave the way for the development of a real-time, low-cost, and reliable tool condition monitoring system. A high degree of correlation is established between the results of the AOE signal and experimental results in identification of tool wear state.

Advanced signal processing and fault diagnosis in condition monitoring

Advanced signal processing methods combined with automatic fault detection enable reliable condition monitoring even when long periods of continuous operation are required. The parameters x (3) and x (4) are very suitable for the condition monitoring of slowly rotating bearings, as although the acceleration pulses are weak and occur at long intervals, the changes in acceleration are rapid and become emphasised upon differentiation of the signal x (2) . Grounds for the need of x (-n) signals, ie integration of displacement n times with respect to time, have been indicated. In addition, derivatives where the order is a real number α or a complex number α+βι have been developed. These signals can be utilised in process or machine operation by combining the features obtained from the derivatives. The importance of each derivative is defined by weight factors. Dimensionless indices are obtained by comparing each feature value with the corresponding value in normal operation. These indices provide useful information on different faults, and even more sensitive solutions can be obtained by selecting suitable features. Widely used root-mean-square values are important in many applications, but the importance of the peak values increases in slowly rotating machines. Further details can be introduced by analysing the distributions of the signals. The features are generated directly from the higher order derivatives of the acceleration signals, and the model can be based on data or expertise. The intelligent models extend the idea of dimensionless indices to nonlinear systems. Variation with time can be handled as uncertainty by presenting the indices as time-varying fuzzy numbers. The classification limits can also be considered fuzzy. The reasoning system will produce degrees of membership for different cases. Practical long-term tests have been performed e.g. for fault diagnosis in bearings, cogwheels, gear boxes, electric motors and supporting rolls, and for cavitation in turbines and pumps.

A joint resonance frequency estimation and in-band noise reduction method for enhancing the detectability of bearing fault signals

The vibration signal measured from a bearing contains vital information for the prognostic and health assessment purposes. However, when bearings are installed as part of a complex mechanical system, the measured signal is often heavily clouded by various noises due to the compounded effect of interferences of other machine elements and background noises present in the measuring device. As such, reliable condition monitoring would not be possible without proper de-noising. This is particularly true for incipient bearing faults with very weak signature signals. A new de-noising scheme is proposed in this paper to enhance the vibration signals acquired from faulty bearings. This de-noising scheme features a spectral subtraction to trim down the in-band noise prior to wavelet filtering. The Gabor wavelet is used in the wavelet transform and its parameters, i.e., scale and shape factor are selected in separate steps. The proper scale is found based on a novel resonance estimation algorithm. This algorithm makes use of the information derived from the variable shaft rotational speed though such variation is highly undesirable in fault detection since it complicates the process substantially. The shape factor value is then selected by minimizing a smoothness index. This index is defined as the ratio of the geometric mean to the arithmetic mean of the wavelet coefficient moduli. De-noising results are presented for simulated signals and experimental data acquired from both normal and faulty bearings with defective outer race, inner race, and rolling element.

Analysis of the structure of vibration signals for tool wear detection

The objective of this work is to develop a reliable tool condition monitoring system (TCMS) for industrial application. The proposed TCMS is based on the analysis of the structure of the tool vibration signals using singular spectrum analysis (SSA) and cluster analysis. SSA is a novel non-parametric technique of time series analysis that decomposes the acquired tool vibration signals into an additive set of time series. Cluster analysis is used to group the SSA decomposition in order to obtain several independent components in the frequency domain that are presented to a feedforward back-propagation (FFBP) neural network to determine the tool flank wear. The results show that this use of SSA and cluster analysis provides an efficient automatic signal processing method, and that the proposed TCMS based on this procedure, is fast and reliable for tool wear monitoring.

Diagnosis Tool for Motor Condition Monitoring

In the modern industrial environment there is increasing demand for automatic condition monitoring. With reliable condition monitoring, faults such as mechanical motor failures could be identified in their early stages and further damage to the system could be prevented. Successful monitoring is a complex and application-specific problem, but a generic tool would be useful in preliminary analysis of new signals and in verification of known theories. A generic condition diagnosis tool is introduced in this paper. The tool is based on discriminative energy functions which reveal discriminative frequency-domain regions where failures can be identified. The tool was applied to induction motor bearing fault detection and succeeded in finding characteristic frequencies which allow accurate detection of bearing faults.

Empirical Model Decomposition Based Time-Frequency Analysis for the Effective Detection of Tool Breakage

Extensive research has been performed to investigate effective techniques, including advanced sensors and new monitoring methods, to develop reliable condition monitoring systems for industrial applications. One promising approach to develop effective monitoring methods is the application of time-frequency analysis techniques to extract the crucial characteristics of the sensor signals. This paper investigates the effectiveness of a new time-frequency analysis method based on Empirical Model Decomposition and Hilbert transform for analyzing the nonstationary cutting force signal of the machining process. The advantage of EMD is its ability to adaptively decompose an arbitrary complicated time series into a set of components, called intrinsic mode functions (IMFs), which has particular physical meaning. By decomposing the time series into IMFs, it is flexible to perform the Hilbert transform to calculate the instantaneous frequencies and to generate effective time-frequency distributions called Hilbert spectra. Two effective approaches have been proposed in this paper for the effective detection of tool breakage. One approach is to identify the tool breakage in the Hilbert spectrum, and the other is to detect the tool breakage by means of the energies of the characteristic IMFs associated with characteristic frequencies of the milling process. The effectiveness of the proposed methods has been demonstrated by considerable experimental results. Experimental results show that (1) the relative significance of the energies associated with the characteristic frequencies of milling process in the Hilbert spectra indicates effectively the occurrence of tool breakage; (2) the IMFs are able to adaptively separate the characteristic frequencies. When tool breakage occurs the energies of the associated characteristic IMFs change in opposite directions, which is different from the effect of changes of the cutting conditions e.g. the depth of cut and spindle speed. Consequently, the proposed approach is not only able to effectively capture the significant information reflecting the tool condition, but also reduces the sensitivity to the effect of various uncertainties, and thus has good potential for industrial applications.

An industrial tool wear monitoring system for interrupted turning

An effective wear-monitoring system for machine tool inserts could yield significant cost savings for manufacturers. Over the years, various methods have been proposed to achieve tool condition monitoring (TCM), and recently sensor-based approaches for indirectly estimating tool wear have become highly popular. One difficulty with collecting sensory information from machine tools is that the signal-to-noise ratio of useful information about the tool wear is extremely poor. This problem can be overcome by using advanced signal-processing methods and also by fusing the information obtained from numerous sensors into a single modelling or decision-making scheme such as neural networks (NNs). Neural networks are known for their capacity to solve problems effectively in cases where theoretical/analytical models cannot be established. Furthermore, NNs can handle noisy and incomplete data such as that typically obtained from machining operations. Although numerous authors have proposed the NN approach for TCM, various problems still hamper a practical method of applying the technique for industrial use. This paper proposes a technique which should overcome these difficulties. A cost-effective and reliable tool condition monitoring system (TCMS) was developed, utilising the advantages of NNs for a typical industrial machining operation. The operation considered is interrupted turning (facing and boring) of Aluminium alloy components for the automotive industry. The development and implementation of various hardware and software components for the proposed technique are described in this paper. The main advantages of the technique are its accuracy, reliability and cost-effectiveness.

Wear monitoring based on the analysis of lubricant contamination by optical ferroanalyzer

Lubricant contamination analysis is one of the common and prospective methods of machine condition monitoring. Wear debris formed in rubbing is a source of valuable information on the wear mechanism and severity, while total oil contamination gives information on oil lubricity. Development of new methods and means of reliable condition monitoring of friction units remains a challenging task for rating the condition of equipment, reducing losses for idle time or failures, and saving lubricants. Despite the fact that direct reading (DR) ferrograph was proposed more than 20 years ago its main principle—accumulation and estimation of deposited particles content—underlies the basis of creation of new effective built-in devices and criteria for condition monitoring. In the presented paper, the principle of operation of the optical ferroanalyzer (OF) jointly developed by V.A. Belyi Metal-Polymer Research Institute of Belarus National Academy of Sciences and Korea Institute of Science and Technology is presented. Optical ferroanalyzer in addition to ferrograph allows us to estimate total contamination of oil, increasing reliability of tribosystem condition monitoring. The results of bench tests of the analyzer are described and example of its application for condition monitoring of engine is shown.

The application of vibration signature analysis and acoustic emission source location to on-line condition monitoring of anti-friction bearings

The mechanical noise, vibration and heat generated by defects in fabrications, process plant and machinery are a measure of component condition, and once characterised allow on-line assessment of defect severity. Unexpected failures which might threaten the entire production process can thereby be avoided. There is an increasing need for reliable and robust condition monitoring instrumentation with on-line data processing, particularly as the trend contunues towards larger production units with little or no standby capacity for use in the event of unscheduled stoppages due to mechanical failure. This article describes the detection of incipient failure of rolling element bearings by kurtosis and the location of fatigue cracks in slowly rotating bearings by acoustic emission