Condition Monitoring Methodology Research Articles

Experimental and numerical study for detection of rail defect

Abstract Condition monitoring methodologies have become an important part in maintenance programs for any type of structure towards prevention of catastrophic accidents. Natural frequency analysis is a useful methodology to evaluate the integrity condition of structural elements, such as: rotor beams, rails and almost every machine component. In this work, two techniques were applied for condition monitoring of rails: numerical, using the Finite Element Method (FEM), and experimental analysis. Sections of a rail 115RE had been characterized in the field for integral track section and laboratory for integral and artificial cracks conditions at different depths, in free-free boundary condition. Numerical simulation was used to compare and validate the experimental analysis. The changes in natural frequencies were observed as a function of the crack depth. It was performed a sensitivity analysis of natural frequency variation due to the influence of the crack depth and the section dimensions in order to explore the behaviour in modes of vibration. In addition, this monitoring technique can be potentially used as a criterion of when is necessary whether or not to eliminate the crack by gridding or replace the entire rail section. Finally, the finite element simulation was validated throughout natural frequencies measurements in the railway network.

Vibration monitoring, fault detection, and bearings replacement of a real wind turbine

Wind turbines are growing rapidly in size and diameter. Nowadays, most wind turbines being installed are around 100 m in height and 80–120 m in diameter. Another important characteristic of wind farms is that they are usually far from urban centers. These peculiarities play an important role when analyzing the operation and maintenance costs and its impact in the wind farm project. In remote centers, it becomes crucial to predict and prevent unnecessary maintenance breakdowns and costs. An efficient solution to prevent faults on wind turbines is through condition monitoring. Faults could be prevented by analyzing data from sensors placed around the wind turbines to measure mainly oil quality, temperature, and vibration. In this paper, accelerometers were placed on the main components of a real wind turbine and a vibration-based condition monitoring methodology was applied using signal processing techniques such as Fourier transform, and envelope analysis with Hilbert transform. A bearing fault was discovered and the vibration characteristics were analyzed before and after the bearing replacement.

Health condition monitoring of insulated joints based on axle box acceleration measurements

This paper presents a health condition monitoring system for insulated rail joints (IRJs) based on axle box acceleration (ABA) measurements. The ABA signals from all the wheels of the measuring train are processed to extract those characteristics that better represent the quality of the IRJ. Then, different indicators are used for damage assessment, the most relevant being a set of frequency bands in the ABA power spectrum. A detection algorithm is proposed based on the derived frequency characteristics of the ABA signal. We compared the responses of IRJs in good condition with those in poor condition. Track inspections were performed to validate the health condition monitoring methodology in different IRJs of the Dutch railway network. The hit rate of IRJs detection was 84% for two validated tracks. The damage assessment procedure allowed to prioritize the IRJs that require maintenance. This information is useful for the railway inframanagers as it allows to predict where safety compromises will be faced.

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.

High-Performance and Energy-Efficient Fault Diagnosis Using Effective Envelope Analysis and Denoising on a General-Purpose Graphics Processing Unit

This paper proposes an effective envelope analysis-based methodology for machinery condition monitoring and validates its efficacy by identifying bearing failures with 1-s acoustic emission (AE) signals sampled at 1 MHz. The proposed condition monitoring methodology of low-speed bearings consists of denoising to improve the signal-noise ratio of the acquired AE signal by employing a soft-thresholding technique with adaptively estimated positive and negative noise levels and an effective envelope analysis to detect the periodic impacts of the AE signals inherent in bearing defects by utilizing a 2-D visualization technique based on the improved residual frequency component-to-peak ratios. Despite the fact that the proposed method shows satisfactory performance for bearing condition monitoring, its computational complexity limits its use in real-time applications. To improve the performance and reduce the energy consumption of the proposed method, this paper proposes an efficient parallel implementation of the proposed method on a general-purpose graphics processing unit (GPGPU) by exploiting the memory hierarchy and the massive parallelism inherent in the proposed method. Experimental results indicate that the proposed GPGPU-based approach achieves an at least 68.9× speed improvement compared to the same sequential implementation on well-known Texas Instruments digital signal processors (TI DSPs). In addition, the proposed GPGPU approach reduces the energy consumption by at least 66% compared to TI DSPs.

Towards Software Performance Monitoring: An Approach for the Aerospace Industry

Software applications are becoming one of the most valuable assets for companies, providing critical capabilities and functionalities to perform a wide range of operations in the industry. This paper aims to provide a view on software application portfolio monitoring and its integration into business intelligence systems for aerospace manufacturing companies. The key research question addressed is how critical software has become for aerospace industry and how software applications could be monitored. This question has been addressed by conducting an in depth review of current literature and by interviewing professionals from different aerospace companies. The results are a set of key findings regarding software impact in aerospace industry, and a monitoring proposal based in a traditional business intelligence architecture.By incorporating condition monitoring methodologies into the software application portfolio of the enterprise, benefits in maintenance budget allocation and risk avoidance are expected, thanks to a more precise and agile way of processing business data. Additional savings should be possible through further application portfolio optimisation.

Reconstruction of road defects and road roughness classification using Artificial Neural Networks simulation and vehicle dynamic responses: Application to experimental data

This paper reports the performance of an Artificial Neural Network based road condition monitoring methodology on measured data obtained from a Land Rover Defender 110 which was driven over discrete obstacles and Belgian paving. In a previous study it was demonstrated, using data calculated from a numerical model, that the neural network was able to reconstruct road profiles and their associated defects within good levels of fitting accuracy and correlation. A nonlinear autoregressive network with exogenous inputs was trained in a series–parallel framework. When compared to the parallel framework, the series–parallel framework offered the advantage of fast training but had a shortcoming in that it required feed-forward of true road profiles. In this study, the true profiles are not available and the test data are obtained from field measurements. Training data are numerically generated by making minor adjustments to the real measured profiles and applying them to a full vehicle model of the Land Rover. This is done to avoid using the same road profile and acceleration data for training and testing or validating the neural network. A static feed-forward neural network is trained and consequently tested on the real measured data. The results show very good correlations over both the discrete obstacles and the Belgian paving. The random nature of the Belgian paving necessitated correlations to be made using their displacement spectral densities as well as evaluations of RMS error percent values of the raw road profiles. The use of displacement spectral densities is considered to be of much more practical value than the road profiles since they can easily be interpreted into road roughness measures by plotting them over an internationally recognized standard roughness scale.

Object shape-based optical sensing methodology and system for condition monitoring of contaminated engine lubricants

Presence of contaminants, such as gasoline, moisture, and coolant in the engine lubricant indicates mechanical failure within the engine and significantly reduces lubricant quality. This paper describes a novel sensing system, its methodology and experimental verifications for analysis of the presence of contaminants in the engine lubricants. The sensing methodology is based on the statistical shape analysis methodology utilizing optical analysis of the distortion effect when an object image is obtained through a thin random optical medium. The novelty of the proposed sensing system lies within the employed methodology which an object with a known periodic shape is introduced behind a thin film of the contaminated lubricant. In this case, an acquired image represents a combined lubricant–object optical appearance, where an a priori known periodical structure of the object is distorted by a contaminated lubricant. The object, e.g. a stainless steel woven wire cloth with a mesh size of 65×65µm2 and a circular wire diameter of 33µm was placed behind a microfluidic channel, containing engine lubricant and optical images of flowing lubricant with stationary object were acquired and analyzed. Several parameters of acquired optical images, such as, color of lubricant and object, object shape width at object and lubricant levels, object relative color, and object width non-uniformity coefficient, were proposed. Measured on-line parameters were used for optical analysis of fresh and contaminated lubricants. Estimation of contaminant presence and lubricant condition was performed by comparison of parameters for fresh and contaminated lubricants. Developed methodology was verified experimentally showing ability to distinguish lubricants with 1%, 4%, 7%, and 10% coolant, gasoline and water contamination individually and in a combination form of coolant (0%–5%) and gasoline (0%–5%).

114 ウェーブレッド変換を用いたタレットパンチプレスの状態モニタリング(セッション4 機械の状態監視)

Immediate detection of abnormalities which occur in a production machine and taking a prompt and appropriate action are essential to maintain. Repair is required before the defective product is made in an actual production plants. This study aims to develop a condition monitoring methodology for a punching machine based on wavelet analysis of the impulsive acceleration response during the punching process. The acceleration response on the die in a turret punch press was measured during a repetitive punching test and analyzed with wavelets whose mother wavelet was the third derivative of the Gaussian function. The signal concentration measure evaluated from the extrema of the wavelet transform showed a considerable correlation with the wear transition, thus, it was suggested that it could be a signature of the tool wear progress and useful as a potential measure for the tool wear monitoring.

Single-parameter fault identification through information entropy analysis at the startup-transient current in induction motors

Opportune diagnosis of rotating machines contributes to avoid expensive reparations and unscheduled shutting downs, and prevents incomings losses. Most of the developed techniques for induction motor condition monitoring fall in one of three classifications: the detection of a single fault by analyzing one or multiple parameters; the detection of different faults by combining multiple parameters and processing techniques; and expert systems that combine several computing-intensive techniques to analyze different electrical and mechanical parameters in order to detect multiple faults. Recent works have been oriented to provide computationally effective diagnostic tools for condition monitoring, which able to discriminate different faults by analyzing a minimum set of parameters. This work presents a methodology for induction motor condition monitoring by analyzing a single parameter. The analysis combines the reconstruction of a single wavelet-packet node with information entropy to obtain one parameter, which allows the detection of different faults quantitatively by analyzing the startup-transient current signal from the induction motor. Experimental results show that the proposed methodology allows the detection of a healthy motor, a motor with one broken bar, a motor with unbalanced mechanical load, and a motor with a faulty bearing in a quantitative way; with a certainty of more than 99.7%.

Review of Techniques for Fault Diagnosis in Damaged Structure and Engineering System

Focus has been made to give an overview of various methodologies used in fault diagnosis and condition monitoring. A crack in vibrating structures can lead to premature failure if it is not detected in time. Researchers have been working on the dynamics of cracked structures for decades to be able to monitor a structure and diagnose fault at the earliest possible stage. An effort has been made in the current paper to understand different techniques and methodologies for fault diagnosis and condition monitoring of damaged structures subjected to varied dynamic loading. The methods used are classical, wavelet transform, and finite element methods, artificial intelligence methods, and numerical and experimental methods. Using classical methods, engineers are able to predict faults. But using artificial intelligence techniques, it is observed that the forecasting time for fault diagnosis improves a lot in comparison to other methodologies.

Functional and advanced functional and causal relationship diagrams for remote condition monitoring

Analysing the condition of an asset is a big challenge as there can be many aspects which can contribute to the overall functional reliability of the asset that have to be considered. Inthis paper we propose a two-step functional and causal relationship diagram (FCRD) to address this problem. In the first step, the FCRD is designed to facilitate the analysis of the condition of an asset by evaluating the interdependence (functional and causal) relationships between different components of the asset with the help of a relationship diagram. This is followed by the advanced FCRD (AFCRD) which refines the information from the FCRD into a comprehensive and manageable format. This new two-step methodology for asset condition monitoring is tested and validated for the case of a water treatment plant.

Recent Progress of Vibration Analysis on Fault Diagnosis for Rolling Bearing

Rolling bearing is extensively used in various areas including shipbuilding, aircraft, mining, manufacturing, agriculture, etc. The breakdowns of the bearings may do unexpected hazardous to the machinery. Therefore, it is crucial for engineers and researchers to monitor the bearing conditions in time in order to prevent the malfunctions of the plants. Hence the vibration analysis accompanying other condition monitoring methodologies has been successfully used in the field of roller bearings fault diagnosis as well as other key components. The fault diagnosis method of roller bearing using vibration analysis was introduced in this paper. And the development trend of vibration analysis was presented. Meanwhile the main existing problems in fault diagnosis of roller bearing were discussed. It concludes that the integration of vibration analysis and other detection approach can provide effective and reliable fault diagnosis results.

An Overview of Progress and New Developments in FTIR Lubricant Condition Monitoring Methodology

Abstract Fourier transform infrared (FTIR) spectroscopy has found an important analytical niche in the field of condition monitoring (CM) of lubricants, lubricant quality being a primary determinant of the wear and operational efficiency of engines and machinery. The ability to track a wide range of functional groups associated with chemical changes in lubricants by FTIR spectroscopy allows the overall quality of lubricants to be assessed and informed decisions made as to whether a lubricant needs to be replaced or not. Newer automated FTIR systems in centralized laboratories are now capable of analyzing oil samples at rates of>120 samples/h, providing trending information on soot, moisture, glycol, oxidative status, anti-wear additives, and nitration, amongst other measures. The current FTIR CM methodology is based on ASTM Standard Practice E2412-10 and is restricted primarily to petroleum or mineral oils; however, the ability to spectrally classify oils could overcome this limitation and broaden the scope of the methodology substantially. Quantitative FTIR methods have come to the fore to carry out more specific measures related to oil condition, specifically the determination of acid content and base content, as well as moisture. These methods have been designed to overcome some key limitations associated with the corresponding ASTM titrimetric procedures and expand the overall utility of FTIR instrumentation in centralized lubricant analysis laboratories. This paper provides an overview of FTIR CM, how it has been and can be further improved, and discusses the recent advances in the newer quantitative FTIR CM methods as well as the issues that need to be addressed to further enhance the utility of FTIR spectroscopy in relation to lubricant analysis.

Principal Component and Hierarchical Cluster Analyses as Applied to Transformer Partial Discharge Data With Particular Reference to Transformer Condition Monitoring

This paper analyses partial discharges obtained by remote radiometric measurements from a power transformer with a known internal defect. Since fingerprints of remote radiometric measurements are not available, the formation of clusters with similar features obtained from captured partial discharge data is crucial. Hierarchical cluster analysis technique is used as a method for grouping different signals. Investigation based on Euclidean and Mahalanobis distance measures and Ward and Average linkage algorithms were performed on partial discharge data pre-processed by principal component analysis. As a result of the analysis, a clear separation of partial discharges emanating from the transformer and discharges emanating from its surrounding is achieved; this in turn should enhance the methodologies for condition monitoring of power transformers.

Development of a fouling monitor for safety-related heat exchangers

Periodic safety reviews (PSRs) are performed to verify and/or improve the safety of operating nuclear power plants. When the PSR of Gori Units 3/4 in South Korea was performed, a few safety-related heat exchangers were pointed out as components in need of an ageing management program. This study shares the motivation with the necessity of the ageing management program. We developed a condition monitoring methodology for identifying one of the ageing mechanisms in the safety-related heat exchangers and named it the fouling monitor. The fouling monitor is composed of (1) the model for determining the test fouling resistance and the projected design fouling resistance at a test condition, and (2) the monitoring and detection of their deviation. The mathematical model for determining the projected design fouling resistance at a test condition needs to be considered carefully because the heat exchanger, and particularly in this study, the letdown heat exchanger, is normally tested under operating conditions that differ considerably from the design conditions. This need was met by developing independent physical and empirical correction methods of which consistency is cross-checked. Either or both correction method(s) convert the design fouling resistance into a projected fouling resistance, which is finally comparable with the test fouling resistance. The monitoring and detection method tracks the deviation of the test fouling resistance and the projected fouling resistance. Considering the stochastic characteristics of fouling phenomena, we adopted the sequential probability ratio test to determine appropriate scheduling for cost- and safety-optimized maintenance of the heat exchanger.

System and methodology for vibration analysis and condition monitoring

A system and methodology for continuous condition monitoring of rotating equipment. Employ adaptive signal processing techniques to determine the RPM of a rotating machine from time-based vibration data. RPM is determined based upon the input of a digitized time-based sample sequence of vibration data acquired directly from a vibration transducer for on-line real-time measurement of the machine RPM. Once RPM is determined, online vibration analysis for a given RPM or set of RPMs may be performed. The present invention extracts characteristic vibration features from vibration data and uses these extracted values to provide condition detection and diagnoses of machine faults.

Condition monitoring of first mode of metal transfer in friction stir welding by image processing techniques

This paper discusses a method for online condition monitoring of the friction stir welding (FSW) process using image processing techniques. Many FSW experiments are carried out at different process parameters and images of the first mode of the weld zone are captured. They are subsequently digitally processed and analysed using MATLAB to study the variations in the quality of weld, subjected to various conditions such as pin failure, and pin depth. This facilitates development of a methodology for online condition monitoring.

Meeting the challenges related to material issues in chemical industries

Reliable performance and profitability are two important requirements for any chemical industry. In order to achieve high level of reliability and excellent performance, several issues related to design, materials selection, fabrication, quality assurance, transport, storage, inputs from condition monitoring, failure analysis etc. have to be adequately addressed and implemented. Technology related to nondestructive testing and monitoring of the plant is also essential for precise identification of defect sites and to take appropriate remedial decision regarding repair, replacement or modification of process conditions. The interdisciplinary holistic approach enhances the life of critical engineering components in chemical plants. Further, understanding the failure modes of the components through the analysis of failed components throws light on the choice of appropriate preventive measures to be taken well in advance, to have a control over the overall health of the plant. The failure analysis also leads to better design modification and condition monitoring methodologies, for the next generation components and plants. At the Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam, a unique combination of the expertise in design, materials selection, fabrication, NDT development, condition monitoring, life prediction and failure analysis exists to obtain desired results for achieving high levels of reliability and performance assessment of critical engineering components in chemical industries. Case studies related to design, materials selection and fabrication aspects of critical components in nuclear fuel reprocessing plants, NDT development and condition monitoring of various components of nuclear power plants, and important failure investigations on critical engineering components in chemical and allied industries are discussed in this paper. Future directions are identified and planned approaches are briefly described