Showing papers on "Condition monitoring published in 2002"

Review of condition assessment of power transformers in service

Abstract: As transformers age, their internal condition degrades, which increases the risk of failure. To prevent these failures and to maintain transformers in good operating condition is a very important issue for utilities. Traditionally, routine preventative maintenance programs combined with regular testing were used. The change to condition-based maintenance has resulted in the reduction, or even elimination, of routine time-based maintenance. Instead of doing maintenance at a regular interval, maintenance is only carried out if the condition of the equipment requires it. Hence, there is an increasing need for better nonintrusive diagnostic and monitoring tools to assess the internal condition of the transformers. If there is a problem, the transformer can then be repaired or replaced before it fails. An extensive review is given of diagnostic and monitoring tests, and equipment available that assess the condition of power transformers and provide an early warning of potential failure.

Method and apparatus for operator condition monitoring and assessment

Abstract: A system using passive infrared imaging of the face and other body parts of an operator to obtain observables by automatically extracting features from a sequence of images, analyzing the extracted features, and then assessing the results for indicators of performance of a task by the operator in order to provide early warning of potential cognitive or motor impairment and thereby facilitate risk reduction and quality maintenance. The infrared condition monitoring system (IR-CMS) serves to a) assess cognitive and/or physical readiness to perform a particular task; b) provide condition assessment feedback to the subject and his appropriate supervisors; c) activate measures to increase short-term alertness and other readiness factors; d) limit potential risks by restricting the subject's access, responsibility, or authority; and e) facilitate rapid medical treatment, evacuation, quarantine, re-training, or counseling as appropriate. The same condition monitoring and assessment system can also be used during training and simulator exercises to evaluate personnel for assignment.

Condition monitoring techniques for electrical equipment-a literature survey

Abstract: Increasing interest has been seen in condition monitoring (CM) techniques for electrical equipment, mainly including transformer, generator, and induction motor in power plants, because CM has the potential to reduce operating costs, enhance the reliability of operation, and improve power supply and service to customers. Literature is accumulated on developing intelligent CM systems with advanced practicability, sensitivity, reliability, and automation. A literature survey is felt necessary with an aim to reflect the state-of-the-art development in this important area. After introducing the concepts and functions of CM, this paper describes the popular monitoring methods for and research status of CM on transformers, generators, and induction motors, respectively. The paper also points out the potential benefits through the utilization of advanced signal processing and artificial intelligence techniques in developing novel CM schemes.

Diagnostic systems and methods for predictive condition monitoring

Abstract: A system for empirically diagnosing a condition of a monitored system. Estimates of monitored parameters from a model of the system provide residual values that can be analyzed for failure mode signature recognition. Residual values can also be tested for alert (non-zero) conditions, and patterns of alerts thus generated are analyzed for failure mode signature patterns. The system employs a similarity operator for signature recognition and also for parameter estimation. Failure modes are empirically determined, and precursor data is automatically analyzed to determine differentiable signatures for failure modes.

Prognostic enhancements to diagnostic systems for improved condition-based maintenance [military aircraft]

Abstract: In recent years, numerous machinery health monitoring technologies have been developed by the US Navy to aid in the detection and classification of developing machinery faults for various Naval platforms. Existing Naval condition assessment systems such as ICAS (Integrated Condition Assessment System) employ several fault detection and diagnostic technologies ranging from simple thresholding to rule-based algorithms. However, these technologies have not specifically focused on the ability to predict the future condition (prognostics) of a machine based on the current diagnostic state of the machinery and its available operating and failure history data. An advanced prognostic capability is desired because the ability to forecast this future condition enables a higher level of condition-based maintenance for optimally managing total life cycle costs (LCC). A second issue is that a framework does not exist for "plug-and-play" integration of new diagnostic and prognostic technologies into existing Naval platforms. This paper outlines such prognostic enhancements to diagnostic systems (PEDS) using a generic framework for developing interoperable prognostic "modules". Specific prognostic module examples developed for gas turbine engines and gearbox systems are also provided.

Adaptive modeling of changed states in predictive condition monitoring

Abstract: An improved empirical model-based surveillance or control system for monitoring or controlling a process or machine (105) provides adaptation of the empirical model (117) in response to new operational states that are deemed normal or non-exceptional for the process or machine. An adaptation decision module (125) differentiates process or sensor upset requiring alerts from new operational states not yet modeled. A retraining module (128) updates the empirical model (117) to incorporate the new states, and a pruning technique optionally maintains the empirical model by removing older states in favor of the added new states recognized by the model.

A DSP-based FFT-analyzer for the fault diagnosis of rotating machine based on vibration analysis

Abstract: A DSP-based measurement system dedicated to the vibration analysis on rotating machines was designed and realized. Vibration signals are on-line acquired and processed to obtain a continuous monitoring of the machine status. In case of a fault, the system is capable of isolating the fault with a high reliability. The paper describes in detail the approach followed to built up fault and non-fault models together with the chosen hardware and software solutions. A number of tests carried out on small-size three-phase asynchronous motors highlight the excellent promptness in detecting faults, low false alarm rate, and very good diagnostic performance.

Condition monitoring of stator windings in induction motors. I. Experimental investigation of the effective negative-sequence impedance detector

Abstract: The former US Bureau of Mines funded a research project aimed at developing the enabling technology for incipient failure prediction in electric power system components as a means of reducing the injuries and fatalities that sometimes occur when equipment malfunctions. Over the ensuing years, interest in this has waxed and waned, but interest has been growing for both civilian and military applications. This paper addresses the level of turn-to-turn insulation deterioration that can be resolved using an online monitoring technique based upon an effective negative-sequence impedance detector. The detection of turn-to-turn defects is especially important because they are believed to represent the beginning stage of most motor winding failures.

Residual signal alert generation for condition monitoring using approximated SPRT distribution

Abstract: A system and method for monitoring a condition of a monitored system. Estimates of monitored parameters from a model of the system provide residual values that can be analyzed using a sequential probability ratio test (“SPRT”). The invention employs empirically derived distributions in the SPRT to provide more accurate and sensitive alerts of impending faults, breakdowns and process deviations. The distributions can be generated from piecewise continuous approximation or spline functions based on the actual distribution of residual data to provide improved computational performance. The distributions can be provided before monitoring, or can be updated and determined during monitoring in an adaptive fashion.

An amplitude modulation detector for fault diagnosis in rolling element bearings

Abstract: As rolling element bearings begin to fail they induce characteristic fault frequencies in the vibration of an electric machine. These characteristic fault frequencies tend to modulate the electric machine's frequencies of natural mechanical resonance. Therefore, an amplitude modulation (AM) detector, similar to the bispectrum, is proposed. This new tool is especially designed to detect instances of AM when the measured magnitude of the characteristic fault frequency itself is not significant (e.g. incipient bearing faults). A normalized version of this detector is also presented to assist in the interpretation of results. Computer simulations as well as actual bearing vibration data are used to confirm the proficiency of this proposed AM detector in identifying bearing faults.

Monitoring and Diagnostic of Transformer Solid Insulation

Abstract: Liberalization of the energy market has put increasing pressure on both electricity producers and distributors for lower costs. Since maintenance is a major expense account, such companies hill be inclined to reduce maintenance budgets. At the same time, increased liability for nondelivered-energy increases the costs of sudden failure of a component. Transformers are such a component; they are often an essential link in the distribution network. In order to reconcile both decreasing maintenance spending and reliable service, condition-based maintenance (CBM) is often proposed. The basis of a successful application of CBM lies in obtaining information on transformers, so that, on the one hand, a critical condition will be noted early enough to take measures and, on the other hand, so that only minimal maintenance is being applied to transformers still in good condition. This paper will review a series of often mentioned techniques in order to assess what the value of this technique will be for CBM and whether it can be used for condition monitoring.

Efficient reactive monitoring

Abstract: Networks are monitored in order to ensure that the system operates within desirable parameters. The increasing complexity of networks and services provided by them increases this need for monitoring. Monitoring consists of measuring properties of the network, and of inferring an aggregate predicate from these measurements. Conducting such monitoring introduces traffic overhead that may reduce the overall effective throughput. This paper studies ways to minimize the monitoring communication overhead in IP networks. We develop and analyze several monitoring algorithms that achieve significant reduction in the management overhead while maintaining the functionality. The main idea is to combine global polling with local event driven reporting. The amount of traffic saving depends on the statistical characterization of the monitored data. We indicate the specific statistical factors that affect the saving and show how to choose the right algorithm for the type, of monitored data. In particular, our results show that for Internet traffic our algorithms can save more than 90% of the monitoring traffic.

Condition Assessment of Power Transformer on-Load Tap-Changers Using Wavelet Analysis and Self-Organizing Map: Field Evaluation

Abstract: An onload tap changer (OLTC) is the most maintenance intensive subassembly on a power transformer. Vibration monitoring is an effective technique that can be used to assess the condition of an OLTC nonintrusively. The authors have developed a condition monitoring system for common types of OLTCs that enables the condition of tap changer contacts and associated drive system to be inferred from vibration signals. A number of prototype systems have been installed onto OLTCs in distribution zone substations for field trials. Particular emphasis has been given to the detection of faults in a particular type of older tap changer that had been prone to a range of faults associated with the switching contacts and drive mechanism. For this type of tap changer, it has been shown to be possible to determine not only that the tap changer is aging but also to identify the particular part that is degrading.

Determination of the optimum routine test and self-checking intervals in protective relaying using a reliability model

Abstract: Relaying reliability is generally separated into the two different aspects of dependability and security. The reliability of a protection relay can be improved by carrying out routine maintenance or by including built-in monitoring and self-checking facilities during the design stages. A Markov model is described in this paper which can be used to examine these features. The model also recognizes common-cause failures, temporary and permanent faults and the associated clearing times, operation of back-up protection, and relay mal-trips. Studies have been conducted to illustrate the expected reliability benefits by inclusion of monitoring and self-checking facilities within the relay.

New fault diagnosis of circuit breakers

Abstract: Wavelet packets and neural networks have been used to analyze the vibration data of circuit breakers for the detection of incipient circuit breaker faults. Wavelet packets are used to convert measured vibration data from healthy and defective circuit breakers into wavelet features. Selected features highlighting the differences between healthy and faulty condition are processed by a back-propagation neural network for classification. Testing has been done for three 66 kV circuit breakers with simulated faults. Detection accuracy is shown to be far better than other classical techniques such as the windowed Fourier transform, stand alone artificial neural networks or expert system. The accuracy of detection for some faults can be as high as 100%.

Condition monitoring of stator windings in induction motors. II. Experimental investigation of voltage mismatch detectors

Abstract: For pt.I see ibid., vol.38, no.5, p.1447-53 (2002). Condition-based maintenance (CBM) of industrial equipment is generally recognized as being the most cost-effective means for improving equipment availability. However, a prerequisite to successful implementation of CBM is a reliable detector of failing components. One such detector, termed the effective negative-sequence impedance, had previously been identified as an indicator of an induction motor stator winding degradation. However, a limitation of this detector is that it may not change in a predictable manner for extremely low levels of deterioration. Presented in this paper is an experimental investigation of a technique for online detection of induction motor stator winding degradation that addresses this difficulty. It begins with a brief description of the detectors, followed by a detailed description of the experimental setup, the experiments conducted, and results.

Gear fault diagnosis using energy-based features of acoustic emission signals

Abstract: In this work, energy-based features are introduced for monitoring and diagnosis of machine conditions in spite of speed and load variations. The basic feature, termed here the energy index (EI), is a statistical measure of relative energy levels of segments of a time domain signal over a cycle. The properties of the EI are discussed and its different forms are derived. A procedure is presented for fault diagnosis of gears using the proposed features. As an illustration, time domain acoustic emission (AE) signals of a test gearbox have been processed to extract these features and to test their relative significance in the diagnostic process. The proposed technique is compared with some of the existing methods using the same AE data for early fault detection. The applicability of the proposed technique is also studied using a set of vibration data of a helicopter drivetrain system gearbox. The results show the effectiveness of the proposed features in monitoring and diagnosis of machine conditions, ...

Wavelet transform with spectral post-processing for enhanced feature extraction

Abstract: The quality of machine condition monitoring techniques as well as their applicability in the industry are determined by the effectiveness and efficiency with which characteristic signal features are extracted and identified. Because of the weak amplitude and short duration of structural defect signals at the incipient stage, it is generally difficult to extract hidden features from the data measured using conventional spectral techniques. A new approach based on a combined wavelet and Fourier transformations is presented in this paper. Experimental studies on a rolling bearing with a localized point defect of 0.25 mm diameter has shown that this new technique provides significantly improved feature extraction capability over the spectral techniques.

Intelligent condition monitoring and asset management. Partial discharge monitoring for power transformers

Abstract: Condition monitoring of power transformers is a significant issue for electrical utilities. Research has demonstrated the efficacy of employing ultra high frequency measurement of partial discharge in the monitoring of transformers. A condition monitoring architecture is described that supports the capture and interpretation of diagnostic data, and provides engineers with meaningful diagnostic advice using intelligent system technologies.

Method for condition monitoring of wind turbine rotor blades in which sound and vibration sensors are used to generate signals that can be compared with an existing database of signals so that rapid damage assessment can be made

Abstract: Method in which rotor blades (1-3) are monitored using sensors for resonance and Eigen frequencies, transmission and reflection spectra after transmission of an excitation signal, or by self-excitation or from operating noise. The received signals and transformed signal spectra are compared with a database of signals derived from model analyses in wind tunnels and data from other wind turbines, both damaged and undamaged. From the comparisons conclusions can be made about the rotor blade states. An Independent claim is made for a device for monitoring the state of wind turbine rotor blades using sound or vibration sensors and one or more actuators attached to relevant signal generating points on the rotor blades.

Enhancement of physics-of-failure prognostic models with system level features

Abstract: To truly optimize the deployment of DoD assets, there exists a fundamental need for predictive tools that can reliably estimate the current and reasonably predict the future capacity of complex systems. Prognosis, as in all true predictions, has inherent uncertainty, which has been treated through probabilistic modeling approaches. The novelty in the current prognostic tool development is that predictions are made through the fusion of stochastic physics-of-failure models, relevant system or component level health monitoring data and various inspection results. Regardless of the fidelity of a prognostic model or the quantity and quality of the seeded fault or run-to-failure data, these models should be adaptable based on system health features such as vibration, temperature, and oil analysis. The inherent uncertainties and variability in material capacity and localized environmental conditions, as well as the realization that complex physics-of-failure understanding will always possess some uncertainty, all contribute to the stochastic nature of prognostic modeling. However, accuracy can be improved by creating a prognostic architecture instilled with the ability to account for unexpected damage events, fuse with diagnostic results, and statistically calibrate predictions based on inspection information and real-time system level features. In this paper, the aforementioned process is discussed and implemented first on controlled failures of single spur gear teeth and then on a helical gear contained within a drivetrain system. The stochastic, physics-of-failure models developed are validated with transitional run-to-failure data developed at Penn State ARL. Future work involves applying the advanced prognostics process to helicopter gearboxes.

Cost-optimal condition-monitoring for predictive maintenance of 2-phase systems

Abstract: The deterioration processes of many industrial systems can be modeled in 2-phases. A 2-phase system begins its life in a new condition where it resides for a random amount of time before progressing to a worn condition where it resides for a random amount of time preceding system failure. If monitoring takes place while the system is in the worn condition, preventive maintenance is performed. This paper analyzes predictive maintenance policies for systems exhibiting 2-phase behavior, and presents cost-minimizing policies, as well as satisfying policies, to determine when monitoring should take place, and for allocating monitoring resources to multiple systems. The solution approach is based on decomposing the expected cost (per unit time) into 2 components: the expected cost due to maintenance actions, and the expected cost due to monitoring actions. This decomposition facilitates the construction of operating-characteristic curves that represent policy performance, and allows evaluation of the policy tradeoffs in many situations including those with constrained or unconstrained monitoring resources, multiple or single systems, and fixed or nonfixed monitoring intervals.

On monitoring transparent optical networks

Abstract: Fault identification and localization problems in optical networks have become crucial. Due to network transparency and high data rates, optical networks are vulnerable to sophisticated attacks on the physical infrastructure, and hence require adequate fault monitoring in order to accurately identify and locate network failures. In transparent optical networks, faults may propagate to various parts of the network from the origin, and multiple alarms can be generated for a single failure. In order to reduce the number of redundant alarms, simplify fault localization, as well as lower financial investment in network monitoring equipment, fault monitor placement should be optimized for a given network. In this paper, we formulate a problem on the optimal placement of network monitoring devices and propose a solution approach. We provide a brief summary of available physical-layer monitoring devices, and present a scheme for optimal monitor placement.

Parametric fault diagnosis for electrohydraulic cylinder drive units

Abstract: A novel model-based methodology for fault diagnosis (FD) of nonlinear hydraulic drive systems is presented in this paper. Due to its linear dependence upon parameters, a second-truncated Volterra nonlinear model is first used to characterize such systems. The versatile order-recursive estimation scheme is employed to determine the values of parameters in the Volterra model. The scheme also avoids separate determination of the model order; thus, the complexity of the search process is reduced. Next, it is shown that the estimated parameters, representing different states of the system, normal as well as faulty conditions, can be used to detect and isolate system faults in a geometric domain. Very promising results are exhibited via simulations as well as laboratory experiments. It is concluded that the developed parametric FD technique has potential to provide efficient condition monitoring and/or preventive maintenance in hydraulic actuator circuits.