Showing papers on "Condition monitoring published in 2006"

Intelligent condition monitoring and fault diagnostic system for preventative maintenance

Abstract: A system for condition monitoring and fault diagnosis includes a data collection function that acquires time histories of selected variables for one or more of the components, a pre-processing function that calculates specified characteristics of the time histories, an analysis function for evaluating the characteristics to produce one or more hypotheses of a condition of the one or more components, and a reasoning function for determining the condition of the one or more components from the one or more hypotheses.

Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring

Abstract: This paper presents a signal analysis technique for machine health monitoring based on the Hilbert-Huang Transform (HHT). The HHT represents a time-dependent series in a two-dimensional (2-D) time-frequency domain by extracting instantaneous frequency components within the signal through an Empirical Mode Decomposition (EMD) process. The analytical background of the HHT is introduced, based on a synthetic analytic signal, and its effectiveness is experimentally evaluated using vibration signals measured on a test bearing. The results demonstrate that HHT is suited for capturing transient events in dynamic systems such as the propagation of structural defects in a rolling bearing, thus providing a viable signal processing tool for machine health monitoring

Development of Acoustic Emission Technology for Condition Monitoring andDiagnosis of Rotating Machines; Bearings, Pumps, Gearboxes, Engines and RotatingStructures.

Abstract: One of the earliest documented applications of acoustic emission technology (AET) to rotating machinery monitoring was in the late 1960s. Since then, there has been an explosion in research- and application-based studies covering bearings, pumps, gearboxes, engines, and rotating structures. In this paper we present a comprehensive and critical review to date on the application of AET to condition monitoring and diagnostics of rotating machinery.

Sensory-Updated Residual Life Distributions for Components With Exponential Degradation Patterns

Abstract: Research on interpreting data communicated by smart sensors and distributed sensor networks, and utilizing these data streams in making critical decisions stands to provide significant advancements across a wide range of application domains such as maintenance management. In this paper, a stochastic degradation modeling framework is developed for computing and continuously updating residual life distributions of partially degraded components. The proposed degradation methodology combines population-specific degradation characteristics with component-specific sensory data acquired through condition monitoring in order to compute and continuously update remaining life distributions of partially degraded components. Two sensory updating procedures are developed and validated using real-world vibration-based degradation information acquired from rolling element thrust bearings. The results are compared with two benchmark policies and illustrate the benefits of the sensory updated degradation models proposed in this paper. Note for Practitioners-The proposed degradation-based prognostic methodology provides a comprehensive assessment of the current and future degradation states of partially degraded components by combining population-specific degradation or reliability information with real-time sensory health monitoring data. It is specifically beneficial for cases where degradation occurs in a cumulative manner and the degradation signal can be approximated by an exponential functional form. To implement this methodology, it is necessary: 1) to identify the physical phenomena associated with the evolution of the degradation process (spalling and wear herein); 2) choose the appropriate condition monitoring technology to monitor this phenomena (accelerometers); 3) identify a characteristic pattern in the sensory information to help develop a degradation signal (exponential growth); and 4) identify a failure threshold associated with the degradation signal. The first step in implementing this prognostic methodology is to obtain prior information related to stochastic parameters f the exponential model. This may require fitting some sample degradation signals with an exponential functional form and noting the values of the exponential parameters, or using subjective prior distributions. The second step is to acquire sensory information and begin updating the prior distribution. The updating frequency will dictate which expressions are used to compute the posterior distributions. Once the posterior means, variances, and correlation are computed, the truncated CDF of the residual life can be evaluated using (10) and (11). Note that the truncation is necessary to preclude negative values of the remaining life. Practitioners can implement this methodology using a simple spreadsheet. Since the residual life distributions are skewed, it is reasonable to utilize the median as a measure of the central tendency and, hence, an alternative estimate for the expected value of the remaining life

Condition monitoring and prognosis of utility scale wind turbines

Abstract: The state of the art in condition monitoring in wind turbines, and related technologies currently applied in practice and under development for aerospace applications, are reviewed. Condition monitoring systems estimate the current condition of a machine from sensor measurements, whereas prognosis systems give a probabilistic forecast of the future condition of the machine under the projected usage conditions. Current condition monitoring practice in wind turbine rotors involves tracking rotor imbalance, aerodynamic asymmetry, surface roughness and overall performance and offline and online measurements of stress and strain. Related technologies for monitoring of load history and fatigue crack growth in aircraft structures are evaluated for their applicability to wind turbine blades. Similarly, condition monitoring practice in wind turbines is compared with monitoring and prognosis in helicopter gearboxes. The state of the art in condition monitoring of electronic controls, power electronics and t...

A survey of efficiency-estimation methods for in-service induction motors

Abstract: Condition monitoring of electric motors avoids severe economical losses resulting from unexpected motor failures and greatly improves the system reliability and maintainability. Efficiency estimation, which shares many common requirements with condition monitoring in terms of data collections, is expected to be implemented in an integrated product. This brings more considerations into the selection of the efficiency-estimation methods. This paper presents the results of an up-to-date literature survey on efficiency-estimation methods of in-service motors, particularly with considerations of the motor-condition-monitoring requirements. More than 20 of the most commonly used methods are briefly described and classified into nine categories according to their physical properties. Six categories of these methods are more related to in-service testing and are compared in a table summarizing the required tests and measurements, intrusion level, and average accuracy. Estimation of the rotor speed and the stator resistance, the two stumbling blocks of various efficiency-estimation methods, is also carefully studied; commonly used methods are summarized. Based on the survey results, four efficiency-estimation methods are suggested as candidates for nonintrusive in-service motor-efficiency estimation and condition-monitoring applications. Another contribution of this paper is that a general approach for developing nonintrusive motor-efficiency-estimation methods is proposed, incorporating rotor speed, stator resistance, and no-load loss estimations

Condition monitoring and control for intelligent manufacturing

Abstract: Monitoring and Control of Machining Precision Manfacturing Process Monitoring with Acoustic Emission Tool Condition Monitoring in Machining Monitoring System for Grinding Processes Condition Monitoring of Rotary Machines Advanced Diagnostic and Prognostic Techniques for Rolling Element Bearings Sensor Placement and Signal Processing for Bearing Condition Monitoring Monitoring and Diagnosis of Sheet Metal Stamping Processes Robust State Indicators of Gearboxes Using Adaptive Parametric Modeling Signal Processing in Manufacturing Monitoring Autonomous Active-Sensor Networks for High-Accuracy Monitoring in Manfacturing Remote Monitoring and Control in Distributed Manufacturing Environment An Intelligent Nanofabrication Probe with Function of Surface Displacement/Profile Measurement Smart Transducer Interface Standards for Condition Monitoring and Control of Machines Rocket Testing and Integrated System Health Management

Artificial neural networks and genetic algorithm for bearing fault detection

Abstract: A study is presented to compare the performance of three types of artificial neural network (ANN), namely, multi layer perceptron (MLP), radial basis function (RBF) network and probabilistic neural network (PNN), for bearing fault detection. Features are extracted from time domain vibration signals, without and with preprocessing, of a rotating machine with normal and defective bearings. The extracted features are used as inputs to all three ANN classifiers: MLP, RBF and PNN for two- class (normal or fault) recognition. Genetic algorithms (GAs) have been used to select the characteristic parameters of the classifiers and the input features. For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine. The roles of different vibration signals and preprocessing techniques are investigated. The results show the effectiveness of the features and the classifiers in detection of machine condition.

Non-Stationary Motor Fault Detection Using Recent Quadratic Time-Frequency Representations

Abstract: As the use of electric motors increases in the aerospace and transportation industries where operating conditions continuously change with time, fault detection in electric motors has been gaining importance. Motor diagnostics in a nonstationary environment is difficult and often needs sophisticated signal processing techniques. In recent times, a plethora of new time-frequency distributions has appeared, which are inherently suited to the analysis of nonstationary signals while offering superior frequency resolution characteristics. The Zhao-Atlas-Marks distribution is one such distribution. This paper proposes the use of these new time-frequency distributions to enhance nonstationary fault diagnostics in electric motors. One common myth has been that the quadratic time-frequency distributions are not suitable for commercial implementation. This paper also addresses this issue in detail. Optimal discrete-time implementations of some of these quadratic time-frequency distributions are explained. These time-frequency representations have been implemented on a digital signal processing platform to demonstrate that the proposed methods can be implemented commercially.

An optimal monitoring program for obtaining Voltage sag system indexes

Abstract: This paper presents a meter placement method for voltage sags monitoring in large transmission systems. An integer programming-based modeling is proposed for choosing the locations of power quality meters. A branch-and-bound-type algorithm is used to solve the optimization problem. A large transmission network is used to validate the method. Stochastic assessment of voltage sags is applied to the test network to obtain simulated monitoring results. Voltage sags system indexes are calculated from monitoring programs designed according to the optimization method. Comparisons with the system indexes obtained from a full monitoring program show the applicability of the method.

A Nonintrusive and In-Service Motor Efficiency Estimation Method using Air-Gap Torque with Considerations of Condition Monitoring

Abstract: Energy usage evaluation and condition monitoring for electric machines are important in industry for overall energy savings. They are often expected to be implemented in an integrated product because of many common requirements such as data collection. Because of the uninterrupted characteristic of industrial processes, traditional methods defined in IEEE Standard 112 cannot be used for these in-service motors. This paper proposes a truly nonintrusive method for in-service motor efficiency estimation based on air-gap torque using only motor terminal quantities and nameplate information, with special considerations of motor condition monitoring requirements. Rotor speed and stator resistance, the stumbling blocks of most in-service testing methods, are extracted from motor input currents instead of being measured. The no-load test, which is required for calculating the rotational loss and core loss, is eliminated by using empirical values. Stray-load loss is assumed according to the motor horse power as suggested in IEEE Standard 112. Finally, the proposed method is validated by testing three induction motors with different configurations. Experimental results show that the proposed method can estimate motor efficiencies with less than 2% errors under normal load conditions.

Maintenance policies for systems with condition monitoring and obvious failures

Abstract: We investigate a maintenance optimization problem with condition monitoring, which allows the decision maker to observe some wear-related variable throughout a system's lifetime to more accurately determine its degree of deterioration. Specifically, we examine the problem of adaptively scheduling observations (both perfect and imperfect) and preventive maintenance actions for a multistate, Markovian deterioration system with obvious failures, such that the long-run average-cost per unit time is minimized. We establish structural properties of the perfect observation-information problem and adjust them for heuristic use in the imperfect observation-information problem. We model both cases as partially observed Markov decision processes and provide numerical examples of optimal and heuristic solutions for both cases.

Enhancement of damage-detection of wind turbine blades via CWT-based approaches

Abstract: In this paper, a continuous wavelet transform-based approach is applied to enhance the damage-detection capability of wind turbine blades. With the time--frequency localization features embedded in wavelets, the time and scale information of the acquired signals can be presented as a visualization scheme, where the condition monitoring of turbine blades can be better realized. Based on these sensor signals, this proposed approach was applied to discriminate the damaged structure from the healthy one under several scenarios. Test results have demonstrated the practicality and advantages of the proposed method for the application considered

Intelligent condition-monitoring and dault diagnostic system

Abstract: A system may include four functions a data collection function (105), apre-processing function (110), an analysis function (115), and a reasoning function (120). In addition, the operation of the functions (105), (110), (115), (120) may be coordinated by a health-monitoring and fault-diagnostic manager (130). Each of the four functions (105), (110), (115), (120) and the manager (130) may be implemented in software, hardware, or any combination of both.

Cost-effective condition-based maintenance using markov decision processes

Abstract: Investigations conducted in several industries indicate that there is no direct relationship between equipment failure and equipment age in the majority of cases. Most failures are caused by events or conditions that occur during component operation and manufacturing processes. Therefore, optimal maintenance decisions should be based on the actual deterioration conditions of the components. Condition-Based Maintenance (CBM) is a methodology that strives to identify incipient faults before they become critical to enable more accurate planning of preventive actions. For the ultimate success of CBM methodology, we must have sound methods for modeling deterioration (the propagation of faulty conditions), the conditions and their effects, and the optimal selection and scheduling of inspections and preventive maintenance actions (the right action at the right time). In this paper, we present a generalized CBM model that can be applied to a wide range of applications. The CBM model includes a stochastic deterioration process, a set of maintenance actions and their effects, and a scheduled inspection policy that identifies the condition of deterioration. Using Markov Decision Processes (MDP), we provide an optimal cost-effective maintenance decision based on the condition revealed at the time of inspection. In addition, we present a procedure for finding optimal inspection schedules

Early classifications of bearing faults using hidden markov models, gaussian mixture models, mel-frequency cepstral coefficients and fractals

Abstract: . Most rotating-machine failures are often linked to bearing failures. Propercondition monitoring on bearings is therefore essential to reduce the duration of machinedown-times. This paper introduces feature extraction methodologies that can facilitateearly detection of bearing faults. The time-domain vibration signals of a rotating ma-chine with normal and defective bearings are processed for feature extraction. Both lin-ear and non-linear features are extracted using Multi-Scale Fractal Dimension (MFD),Mel frequency Cepstral Coefficients and kurtosis. The extracted features are then usedto classify faults using Gaussian Mixture Models (GMM) and hidden Markov Models(HMM). Results demonstrate that HMM outperforms GMM in classification of bearingfaults. However, the disadvantage of HMM is that it is computationally expensive totrain compared to GMM.Keywords: Multi-scale fractal dimension, Hidden Markov models, Gaussian mixturemodels 1. Introduction. Rotating machines are the most widely used components in variousindustrial applications ranging from system maintenance to process automation. Mostmachine failures are linked to bearing failures [1],whichoftenresultinlengthyindustrialdowntime that has economic consequences. Bearing faults induce high vibrations whichgenerate noise and lead to malfunctions in the rotating machinery. There is therefore ahigh demand for a cost effective and automated condition monitoring system that candetect faults as early as possible. The early identification of defects in a machine canreduce offline time, maintenance periods, avoid accidents and catastrophic break-down.An automated condition monitoring of bearings is necessary as manual checks may takean unacceptably long duration resulting in money losses. Vibration-based condition mon-itoring is the most popular approach and hasbeenusedextensivelyinvariousbearingcondition monitoring techniques [1, 2]. Vibrations can also be used to detect existence offaults such as mass imbalance, shaft misalignment and gear failures by simply comparingthe vibration signals of a machine operating in faulty conditions and in normal, un-faultedconditions. There are several causes of bearing failure such as incorrect design, acid corro-sion, poor lubrication and plastic deformation [3]. Damage in bearings is typically on therolling element, inner race or outer race[1]. The difficulty of fault detection in bearings

Transient event detection for nonintrusive load monitoring and demand side management using voltage distortion

Abstract: This paper describes a simple system that can be used for autonomous demand-side management in a load site such as a home or commercial facility. The system identifies the operation of individual loads using transient patterns observed in the voltage waveform measured at an electric service outlet. The theoretical foundation of the measurement process is introduced, and a preprocessor that computes short-time estimates of the spectral content of the voltage waveform is described. The paper presents several example measurements demonstrating the ability of the system to obtain estimates of the spectral content of the voltage waveform.

Data fusion/data mining-based architecture for condition-based maintenance

Abstract: Condition-based maintenance (CBM) is a maintenance philosophy wherein equipment repair or replacement decisions are based on the current and projected future health of the equipment. The constituents and sub-processes within CBM include sensors and signal processing techniques that provide the mechanism for condition monitoring, and decision support models. Since past research has been dominated by condition monitoring techniques for specific applications, the maintenance community lacks a generic CBM architecture that would be relevant across different domains. This paper attempts to fulfil that need by proposing a combined data fusion/data mining-based architecture for CBM. Data fusion, which is extensively used in defence applications, is an automated process of combining information from several sources in order to make decisions regarding the state of an object. Data mining seeks unknown patterns and relationships in large data sets; the methodology is used to support data fusion and model generation at several levels. In the architecture, methods from both these domains analyse CBM data to determine the overall condition or health of a machine. This information is then used by a predictive maintenance model to determine the best course of action for maintaining critical equipment.

Dynamic Monitoring of Bridges Using a High-Speed Coherent Radar

Abstract: Remote dynamic monitoring of bridges by a high-speed interferometric radar is proposed. The equipment is a continuous-wave step-frequency radar with very fast frequency hopping that is capable of sampling the structure at a rate high enough for transient analysis of motion through phase comparison of successively acquired images. An experimental test carried out on a highway bridge forced by vehicular traffic is presented