Condition Monitoring Procedure Research Articles

Combining Wavelets and AR Identification for Condition Monitoring of Electric-cam Mechanisms Using PLCopen Readings of Motor Torque

This paper addresses the problem of monitoring the state of health of electric motor driven mechanisms. The proposed condition monitoring procedure belongs to the data-driven methods and employs a combination of wavelet analysis and autoregressive model identification. It exploits the fact that the torque motor signal is a readily available measurement in industrial computers complying with the PLCOpen standard and how motion controllers execute electric cams. In particular, the torque provided by the PLC is represented as the sum between the ideal torque and an additional contribution that contains information about mechanism health condition. The procedure completely removes the ideal torque and analyzes the residual component to highlight and classify possible fault conditions. The described condition monitoring procedure is tested on real data in a laboratory setup.

A novel procedure for identifying the parameters of the single-diode model and the operating conditions of a photovoltaic module from measured current–voltage curves

Photovoltaic (PV) module measured current–voltage curves together with the mathematical single-diode model are potential tools for PV system condition monitoring. Changes in model parameters can reveal module degradation and aging. However, all the parameter values change with varying operating irradiance and temperature, so they should also be identified. Unfortunately, useful operating condition measurements are rarely available in PV power plants. Thus, it is necessary to identify both operating conditions and model parameters directly from measured current–voltage curves. Only one such method has been reported in literature, however, using approximate explicit equations. The present work proposes a novel fitting procedure that identifies both single-diode model parameters and PV module temperature and irradiance utilizing an iterative method instead of approximate equations, which guarantees accurate results. The procedure enables degradation and aging analyses using only the internal electrical current–voltage curve measurements of the inverter. The results showed that the identified irradiance and temperature accurately reproduced the measured ones in all outdoor conditions. The stable identification of other single-diode model parameters, of which series resistance is especially suitable for detecting aging, confirmed the functionality of the procedure for condition monitoring.

A human safety approach to safe maintenance

The deterioration of the technical condition of plant equipment is a common phenomenon today. This is increasingly contributing to breakdowns and accidents, according to the experience of national and international authorities. Plants that are operating with an inadequate maintenance strategy are nowadays approaching the end of their design life and thus represent an increased risk of accidents. Ageing processes are often accelerated by operators' underestimation of the effects of stresses and strains on equipment and the extent of degradation mechanisms, and consequently by inadequate design and operation of condition monitoring and maintenance procedures. The aim of our work is to investigate domestic and foreign trends in the light of modern maintenance optimization methods and to make related recommendations for optimizing maintenance scheduling for domestic employers.

Contribution to the analysis of grinder vibrations using condition monitoring procedures based on vibration measurements

The article deals with the vibration of the workpiece on the grinder using a cBN grinding wheel. The workpieces were made of hardened steel with a hardness of 62 HRC and ceramics: Al2O3, SiC, Si3N4, and ZrO2/Y2O3. Grinding was performed at different infeed rates during 5 consecutive passes. Up and down grinding were also evaluated. Measured realizations of the acceleration of the workpiece during grinding were processed in the time and frequency domain and compared with each other. We consider acceleration patterns to be diagnostic signals that will help to understand the process of chip formation during grinding as well as the process of damaging parts made of the tested materials.

A biztonságos karbantartás humánbiztonsági szempontú megközelítése

Deterioration of the technical condition of plant equipment is a common phenomenon nowadays. According to the experience of authorities, all this contributes to the occurrence of malfunctions and accidents. Plants that operate with an inadequate conservation strategy are now nearing the end of their design life, thus posing an increased accident risk. Aging processes are often accelerated by the operator's underestimation of the effects of equipment stress and the extent of deterioration mechanisms, and the consequent inadequate design and operation of condition monitoring and maintenance procedures. The aim of our work is to examine trends in the light of modern maintenance optimization methods and, in connection with this, to make proposals for the optimization of maintenance scheduling for employers.

A Novel Condition Monitoring Procedure for Early Detection of Copper Corrosion Problems in Oil-Filled Electrical Transformers

The negative impacts of catastrophic fire and explosion accidents due to copper corrosion problems of oil-filled electrical transformers are still in the spotlight due to a lack of effective methods for early fault detection. To address this gap, a condition monitoring (CM) procedure that can detect such problems in the initial stage is proposed in this paper. The suggested CM procedure is based on identified measurable variables, which are the relevant by-products of the corrosion reaction, and utilizes an Early Fault Diagnosis (EFD) model to detect and solve the copper corrosion problems. The EFD model includes a fault trend chart that can track a fault progression during the useful life of transformers. The purpose of this paper is to verify and validate the effectiveness of the suggested CM procedure by an empirical study in a power plant. The result of applying this procedure was early detection of copper corrosion problems in two transformers with suspected copper corrosion propagation from a total of 84. The corrective action was adding an optimized amount of a passivator, an anticorrosion additive, to suppress the corrosion reaction at the correct time. The main conclusion of this study is the importance of early detection of transformer faults to avoid the negative impacts on societal, company, and individual levels.

Predicting Temperature Profile on the Surface of a Switched Reluctance Motor Using a Fast and Accurate Magneto-Thermal Model

In this article, a simplified 3D magneto-thermal model to study the temperature distribution over the housing of a Switched Reluctance Machine (SRM) is proposed. The main objective of such model is to study the temperature distribution profile on the body of the machine under various working conditions in order to introduce a temperature based signature to develop condition monitoring procedures. The analysis of fluid flow and heat transfer inside the SRM supports the hypothesis that the effect of turbulent flow of the fluid and the complex heat transfer phenomenon of the rotating parts can be simplified to a heat flux boundary condition representation of the heat loss. The accuracy of the model is validated through simulation and experimental results on a prototype SRM. The developed simplified model can be used as for prediction of temperature profile on SRM surface, well suited for fault diagnosis in SRM. The proposed methodology can be modified applied in developing a thermal model for other types of electric machines.

Condition monitoring of electric-cam mechanisms based on Model-of-Signals of the drive current higher-order differences

Condition monitoring of electric motor driven mechanisms is of great importance in industrial machines. The knowledge of the actual health state of such components permits to address maintenance policies which results in better exploitation of their actual operational life span and consequently in maintenance cost reduction. In this paper, we exploit the way electric cams are implemented on the vast majority of PLC/Motion controllers to develop a suitable condition monitoring procedure. This technique relies on computing the higher-order differences of the current absorbed by slave motors to get signals that do not depend on a priori knowledge of the cam trajectory and of the mechanism nominal model. Subsequently, we will use these data in the Model-of-Signals framework, to gather information on the mechanism’s health condition, which in turn can be used to perform predictive maintenance policies. The differenced signal is modelled as an ARMA process and the model capabilities in condition monitoring are then shown in simulation and experimental application. Besides, this framework allows exploiting the edge-computing capabilities of the machinery controllers by implementing recursive estimation algorithms.

Testing for detection of crack in rotor using vibration analysis: an experimental approach

Purpose Many incidents of rotor failures are reported due to the development and propagation of the crack. Condition monitoring is adopted for the identification of symptoms of the crack at very early stage in the rotating machinery. Identification requires a reliable and accurate vibration analysis technique for achieving the objective of the study. The purpose of this paper is to detect the crack in the rotating machinery by measuring vibration parameters at different measurement locations. Design/methodology/approach Two different types of cracks were simulated in these experiments. Experiments were conducted using healthy shaft, crack simulated shaft and glued shaft with and without added unbalance to observe the changes in vibration pattern, magnitude and phase. Deviation in vibration response allows the identification of crack and its location. Initial data were acquired in the form of time waveform. Run-up and coast-down measurements were taken to find the critical speed. The wavelet packet energy analysis technique was used to get better localization in time and frequency zone. Findings The presence of crack changes the dynamic behavior of the rotor. 1× and 2× harmonic components for steady-state test and critical speed for transient test are important parameters in condition monitoring to detect the crack. To separate the 1× and 2× harmonic component in the different wavelet packets, original signal is decomposed in nine levels. Wavelet packet energy analysis is carried out to find the intensity of the signal due to simulated crack. Originality/value Original signals obtained from the experiment test set up may contain noise component and dominant frequency components other than the crack. Wavelet packets contain the crack-related information that are identified and separated in this study. This technique develops the condition monitoring procedure more specific about the type of the fault and accurate due to the separation of specific fault features in different wavelet packets. From the experiment end results, it is found that there is significant rise in a 2× energy component due to crack in the shaft. The intensity of a 1× energy component depends upon the shaft crack and unbalance orientation angle.

Effect of Sensor Type and Positioning on Features for Condition Monitoring of a Mechatronic System

In this paper, a methodology is described able to give general suggestions and guide lines in choosing the most suitable parameter, measuring strategy and the feature set for possible use in a condition monitoring procedure. The tests have been carried out on a mechatronic system in real scale, realizing a sinusoidal motion of its end effector. Internal sensors to the controller have been used (encoder and current sensors) and external to it (laser Doppler vibrometer and three-axis accelerometer). A kinematic and dynamic model of the mechanical linkage of the automated system allows the user to correlate the measurements and to compute the other quantities, even though of different type and/or measured in other point of the device. The most suitable quantities and features have been recognized with respect to the capability of identifying different operating statuses of the device, often independently on the measuring point, if the simulation model is used.

Review and comparison of tacholess instantaneous speed estimation methods on experimental vibration data

Abstract Instantaneous speed estimation has become a key part of many condition monitoring procedures for rotating machinery. The ability to track the rotational speed of a system is a critical requirement for the majority of vibration-based condition monitoring methods. Information about the speed enables compensating for potential speed variations that would otherwise impair conventional frequency-based methods. The problem of instantaneous speed estimation based on the vibration signals themselves is one that has received a significant amount of attention in recent years. Installing encoders or tachometers has become a lot less attractive due to the potential cost savings that can be obtained by simply utilizing an accelerometer instead. However, trying to find a speed estimation method that fits a certain application “best” is not so straightforward if one inspects the available literature. It turns out that there are many articles that present slight variations or extensions to already existing techniques. This paper targets a general overview of the available knowledge regarding vibration-based speed estimation techniques. It also aims to review some of the most commonly used techniques by means of a performance comparison of seven speed estimation methods on three different experimental data sets. The resulting speed estimation data of all tested methods is made publicly available such that it can help in forming a benchmark for future speed estimation methods.

Acoustic Emission-Based Similarity Analysis: A Baseline Convergence Algorithm

Condition monitoring has been widely employed to monitor critical components for drivetrains of machinery, whose malfunctioning will inevitably cause unexpected downtime and an increase of maintenance costs. This paper presents a practical condition monitoring technique, which uses a procedure consisting of a baseline definition process, similarity analysis collated with bathtub curve and maintenance decision-making support to enhance the reliability of the machinery. The proposed condition monitoring procedure features the benefits of being scalable and adaptable to multiple sensory technologies including Vibration, Acoustic Emission and Audible Acoustics. Validation of the convergence methodology is performed on a low-speed rotating mechanism. The outcome defines a process of baseline generation and identification of deviations from normal operating conditions.

Failure analysis of a 40 ton crane hook at a Hot Strip Mill

There have been accounts of repeated failure of crane hooks at the coil yard of a Hot Strip Mill which pose a serious threat to safety in the area. More than 4 hooks failed in less than 5 years. The crane hook (rated for 36000 kg) failed from the threaded shank while lifting a load of 18143 kg. The metal in the hook was revealed by chemical analysis to be killed IS: 4367 20C15 steel. The hook rod failed from a step where there was a cross sectional change and the locations were associated with machining and chatter marks. Such cross-sectional changes are the potential sites of stress concentrations leading to crack initiations. Fracture surfaces of broken pieces of hook reveal initiation of beach marks from both sides with granular rough surface at the middle of fracture zone. Beach marks initiated from both sides indicate origin of reverse bending fatigue. Distinct granular rough zone at the middle is due to final brittle fracture. Microstructure of the polished sample revealed numerous inclusions which indicate that the steel was not clean .Such a huge number of inclusions are not desirable as they can act as stress concentration sites and lead to fatigue crack initiation. Etched microstructure of failed hook reveals coarse cast structure having inhomogeneous microstructure with a mixture of ferrite and pearlite (which meant lower fatigue strength). This inhomogeneous coarser cast structure is outcome of lower reduction ratio during rolling followed by improper heat treatment process. The fracture was concluded to have occurred due to stress concentration from the step region due to inferior material (Inclusion and Improper Heat treatment process). Preventive maintenance and condition monitoring procedures should be applied to identify and minimize the risk(s) Establishment of an NDT procedure for regular basis inspection of the structural members (welded joints and hookshaped steel rods during incoming inspection and in-service).

Residual stresses in condition monitoring and repair of thermal power generation components

Residual stresses have a significant impact on fatigue and fracture of engineering components and structures, with an effect that is largely dependent on the sign of the residual stress relative to that of the applied stress, i.e. on whether they add to, or subtract from, the applied stress. The present paper will emphasise the importance of detailed knowledge of residual stresses to applications in thermal power generation. The context of the examples is condition monitoring and repair procedures where assessment of the influence of residual stress fields is important to both fatigue and fracture performance, and to certification of the repair procedure itself. The main conclusion in the paper is that the innovative use of solid-state friction taper hydro-pillar processes can offer additional capability in condition monitoring of through-thickness creep damage in thermal power plant, as well as provide cost-effective local repair of creep or fatigue damage in, for example, thick-walled steam pipe and blade-disc attachment holes.

Motor current signature analysis for gearbox condition monitoring under transient speeds using wavelet analysis and dual-level time synchronous averaging

This paper focuses on analyzing motor current signature for fault diagnosis of gearboxes operating under transient speed regimes. Two different strategies are evaluated, extensively tested and compared to analyze the motor current signature in order to implement a condition monitoring system for gearboxes in industrial machinery. A specially designed test bench is used, thoroughly monitored to fully characterize the experiments, in which gears in different health status are tested. The measured signals are analyzed using discrete wavelet decomposition, in different decomposition levels using a range of mother wavelets. Moreover, a dual-level time synchronous averaging analysis is performed on the same signal to compare the performance of the two methods. From both analyses, the relevant features of the signals are extracted and cataloged using a self-organizing map, which allows for an easy detection and classification of the diverse health states of the gears. The results demonstrate the effectiveness of both methods for diagnosing gearbox faults. A slightly better performance was observed for dual-level time synchronous averaging method. Based on the obtained results, the proposed methods can used as effective and reliable condition monitoring procedures for gearbox condition monitoring using only motor current signature.

Development of a low cost test rig for standalone WECS subject to electrical faults

In this paper, a contribution to the development of low-cost wind turbine (WT) test rig for stator fault diagnosis of wind turbine generator is proposed. The test rig is developed using a 2.5kW, 1750 RPM DC motor coupled to a 1.5kW, 1500 RPM self-excited induction generator interfaced with a WT mathematical model in LabVIEW. The performance of the test rig is benchmarked with already proven wind turbine test rigs. In order to detect the stator faults using non-stationary signals in self-excited induction generator, an online fault diagnostic technique of DWT-based multi-resolution analysis is proposed. It has been experimentally proven that for varying wind conditions wavelet decomposition allows good differentiation between faulty and healthy conditions leading to an effective diagnostic procedure for wind turbine condition monitoring.

Static load testing with temperature compensation for structural health monitoring of bridges

The paper presents a series of repeated static loading tests on a prestressed concrete beam, which was originally part of a real bridge and then subjected to stepwise artificial damage. The tests were done during a one-month period that four levels of damage were introduced by cutting tendons until visible cracking occurred. The deflection line was measured by means of several displacement sensors and the retrieved information is used in different ways for damage detection.At first, the sensor spacing requirement is analyzed with respect to measurement accuracy as well as necessary resolution for the numerical derivations of the deflection line to obtain the rotational angle and the curvature of the beam. These derived quantities may be used as damage indicators in addition to the deflection.Damage of concrete goes very often along with non-linear phenomena like cracking of concrete and plastic strain of reinforcement steel. These effects are discussed and their influence on the repeated loading tests as well the test procedure for condition monitoring is deployed. Progressive damage goes along with progressive sagging of the bridge due to gravity, which can also be used as damage indicator.Finally, the effect of varying outdoor temperatures are discussed and assessed. Though these effects can be reduced by choosing cloudy days without high temperature changes and without high solar irradiation, the outdoor temperature is never constant. Hence, a compensation algorithm is proposed which reflects the measured data according to a reference temperature. This compensation visibly improved the regularity of data.

Local Fault Assessment in a Helical Geared System via Sound and Vibration Parameters Using Multiclass SVM Classifiers

Abstract A gear system transmits power by means of meshing gear teeth and is conceptually simple and effective in power transmission. Thus typical applications include electric utilities, ships, helicopters, and many other industrial applications. Monitoring the condition of large gearboxes in industries has attracted increasing interest in the recent years owing to the need for decreasing the downtime on production machinery and for reducing the extent of secondary damage caused by failures. This paper addresses the development of a condition monitoring procedure for a gear transmission system using artificial neural networks (ANNs) and support vector machines (SVMs). Seven conditions of the gear were investigated: healthy gear and gear with six stages of depthwise wear simulated on the gear tooth. The features extracted from the measured vibration and sound signals were mean, root mean square (rms), variance, skewness, and kurtosis, which are known to be sensitive to different degrees of faults in rotating machine elements. These characteristics were used as an input features to ANN and SVM. The results show that the multilayer feed forward neural network and multiclass support vector machines can be effectively used in the diagnosis of various gear faults.

AStrion strategy: from acquisition to diagnosis. Application to wind turbine monitoring

This paper proposes an automatic procedure for condition monitoring. It represents a valuable tool for the maintenance of expensive and spread systems, such as wind turbine farms. Thanks to data-driven signal processing algorithms, the proposed solution is fully automatic. The paper briefly describes all of the steps involved in the processing, from pre-processing of the acquired signals to interpretation of the generated results. It starts with an angular resampling method with speed measurement correction. Next comes a data validation step, in both time/angular and frequency/order domains. After the pre-processing, the spectral components of the analysed signal are identified and classified in several classes, from sine wave to narrowband components. This spectral peak detection and classification allows for the extraction of the harmonic and side-band series, which may be part of the spectral content of the signal. Moreover, the detected spectral patterns are associated with the characteristic frequencies of the investigated system. Based on the detected side-band series, full-band demodulation is performed. At each step, the diagnosis features are computed and dynamically tracked, signal by signal. Finally, system health indicators are proposed to provide a conclusion on the condition of the investigated system. Altogether the abovementioned steps create a self-sufficient tool for the robust diagnosis of mechanical faults. The paper presents the performance of the proposed method on real-world signals from a wind turbine drive train.

Current signature analysis and its application in the condition monitoring of wind turbine for rotor faults

This paper proposes a condition monitoring technique using current signature analysis for diagnosis of Rotor eccentricity fault in Squirrel cage induction generators used in wind power systems. The proposed method measures the current characteristic frequencies of the excitations generated by a Wind turbine generator. The excitations generated by wind turbine generator eccentricity fault are extracted and analyzed under no load, full load and varying wind conditions. Side bands are calculated using FFT approach. Fault frequencies are further analyzed using wavelet-based analysis for steady state and dynamic conditions. Multi-resolution mean power indicator has been used for the quantification of the fault. It has been experimentally proven that for varying wind conditions wavelet decomposition allows good differentiation between faulty and healthy conditions leading to an effectual diagnostic procedure for condition monitoring.