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Prognostics and Health Management (PHM), 2011 IEEE Conference on

Date 20-23 June 2011

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Displaying Results 1 - 25 of 54
  • Interpolated FFT for real-time detection of belt slip in automotive electric power generation and storage system

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (394 KB) |  | HTML iconHTML  

    Belt slip is an important type of mechanical fault in the automotive electric power generation and storage (EPGS) system. This paper proposes a robust and sensorless solution for detecting serpentine belt slip. The speed of the claw-pole alternator is directly extracted from the rectifier ripple frequency from the alternator output voltage. The estimated alternator speed is then continuously compared with the engine speed to detect possible belt slip. The proposed method is implemented in a real-time fashion on an EPGS test bench. The experimental results show that the method is able to estimate the alternator speed accurately even during fast mechanical transients. The belt-slip fault can thus be reliably detected within a wide speed range. Although originally developed for automotive alternators, the sensorless speed estimation method can be also applied to other AC generators with a DC-link rectifier. View full abstract»

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  • Prognostics and life beyond 60 years for nuclear power plants

    Publication Year: 2011 , Page(s): 1 - 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (996 KB) |  | HTML iconHTML  

    Safe, secure, reliable, and sustainable energy supply is vital for advanced and industrialized life styles. To meet growing energy demand there is interest in longer-term operation for the existing nuclear power plant fleet and enhancing capabilities in new build. There is increasing use of condition-based maintenance for active components and growing interest in deploying on-line monitoring instead of periodic in-service inspection for passive systems. Opportunities exist to move beyond monitoring and diagnosis based on pattern recognition and anomaly detection to prognostics with the ability to provide an estimate of remaining useful life. The adoption of digital I&C systems provides a framework within which added functionality including on-line monitoring can be deployed, and used to maintain and even potentially enhance safety, while at the same time improving planning and reducing both operations and maintenance costs. View full abstract»

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  • Using PHM to meet availability-based contracting requirements

    Publication Year: 2011 , Page(s): 1 - 12
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (505 KB) |  | HTML iconHTML  

    "Availability-based" contracting originated because customers with high availability requirements are in many cases interested in migrating from buying the actual system to buying the availability of the system. A well-known example of availability-based contracting is Performance Based Logistics (PBL). Prognostics and Health Management (PHM) methods are incorporated into systems to avoid unanticipated failures that can potentially impact system safety, result in additional life cycle cost, and/or adversely affect the system availability. While predicting the availability of a system based on known or predicted system parameters is relatively straightforward and can be accomplished using existing methods; determining the system parameters that result in a desired availability is not and is generally performed using "brute force" search-based methods that become quickly impractical for designing systems with more than a few variables and when uncertainties are present. This paper presents the application of PHM within a "design for availability" approach that uses an availability requirement to predict the required logistics, design (including reliability) and operation parameters with and without the application of PHM methods. A life cycle cost analysis is used to quantify trade-offs of using PHM methods versus more traditional maintenance approaches in the context of availability contracts. View full abstract»

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  • Using real options to manage condition-based maintenance enabled by PHM

    Publication Year: 2011 , Page(s): 1 - 7
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (938 KB) |  | HTML iconHTML  

    This work proposes a new economic approach that can form a cost-benefit-risk basis for optimum decision making for systems with prognostic capabilities, and a method to assess the value of PHM for its user after a prognostic indication. PHM potentially enables performance based logistics, condition-based maintenance, and reduced life cycle cost. When an anomaly is detected in a system, and the remaining useful life is estimated, the user has to make a decision about how to operate or manage the system given a set of constraints or requirements (e.g., to maximize availability). This paper proposes a new economic basis for evaluating the flexibility enabled by prognostic and health management systems. The proposed framework is based on Real Options theory for valuating the options arising through the use of PHM. In the context of PHM an option represents the purchase of an opportunity to take a particular action in the future. The underlying assets are not tradable securities (as they would be in financial options), but rather, they are cost avoidance opportunities or mission values. We provide two potential applications to illustrate the new model for electronic systems in a commercial aircraft used by a commercial airline, and wind farms. View full abstract»

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  • Model based POD techniques for enhancing reliability of steam generator tube inspection

    Publication Year: 2011 , Page(s): 1 - 7
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (571 KB) |  | HTML iconHTML  

    In recent times, we have seen a dramatic increase in the need for prognostics and health management of steam generator (SG) tubes in nuclear power plants. Faster and cheaper implementations of new inspection technologies, including probe designs and signal analysis systems are required. The reliability of these new designs can be assessed by a quantitative probability of detection (POD) study. It is known that there are a number of uncertainties (variabilities or noise), such as system/probe coil construction, inspection procedure, human factors, etc., that can perturb measurements, and thereby affect the accept/reject decision. In contrast to conventional approaches, this paper describes a model based POD evaluation method, to study the effect of single sources of uncertainty on the POD, which in turn can be used to optimize probe/system design and operation. An uncertainty factor with dramatic effect on POD can be identified as a "sensitive" factor in the probe/system design or measurement. View full abstract»

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  • A robust processing technique for trend prognosis On Mechanical Coupling for Common Remotely Operated Weapon system (CROWS)

    Publication Year: 2011 , Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (716 KB) |  | HTML iconHTML  

    A robust processing technique for diagnosing and predicting a recipient fault of static components of mechanical systems is presented. In particular, we demonstrate the technique (methodology) to the loosened bolts on the elevation control motor (ECM) of the Common Remotely Operated Weapon Station (CROWS) in order to prevent a disastrous fault or failure in the gear mechanism that loose bolts could cause. Our method, called inverse normalized energy, uses the "symlet" wavelet to de-noise non-stationary vibration signals from a tri-axial accelerometer mounted on the ECM. We calculated the inverse of the ratio of the normalized energy of the "baseline," in which all the bolts were tightened, to cases in which the bolts were loosened to different levels of torque. The normalized energy signals were calculated from the output of Fast Fourier Transform (FFT) spectral components at the frequency band of the residual signals - the difference between the "raw" experimental data and the de-noised data. We conducted a series of controlled experiments where we deliberately loosened the ECM bolts to demonstrate the system's diagnostic and prognostic capability. Based on the experimental data and results from our method, we showed that we can detect a fault and the trend of the loosening bolts on the weapon station. If faults are detected early enough, appropriate measures can be to taken to enhance the reliability of the weapon station. This robust technique may also be well suited for detecting the fault of components of mechanical systems in manufacturing and heavy fixed machinery. View full abstract»

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  • Magneto-Optic Imaging for aircraft skins inspection: A probability of detection study of simulated and experimental image data

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1425 KB) |  | HTML iconHTML  

    The increasing fleet of aging aircrafts has resulted in an increasing demand for cost effective nondestructive evaluation (NDE) techniques that are accurate, reliable and easy to use. Magneto-Optic Imaging (MOI) is such a technique, which has gained wide acceptance for detection of both surface and subsurface defects in multi-layer aircraft structures. The main advantage of MOI is rapid inspection and ease of interpreting image data in contrast to complex impedance signals from conventional eddy current instruments. One missing piece of the puzzle for advanced MOI systems is how to quantitatively analyze the MO images and understand the detectability limits when image data are acquired under varying operational conditions. This paper presents a probability of detection (POD) study that is conducted using both simulation model-predicted and experimental MO image data. Simulated panels from 3-D FEM model and experimental panels with machined defects are used to generate data for data interpretation by human inspectors or automated systems and subsequently for POD studies. The POD curves demonstrate the merits in optimizing inspection parameters that maximized the performance of current MOI systems. Parameters quantifying the detectability of MO image data using skewness functions are also presented and discussed. View full abstract»

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  • Improving system health monitoring with better error processing

    Publication Year: 2011 , Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (316 KB) |  | HTML iconHTML  

    To help identify unexpected software events and impending hardware failures, developers typically incorporate error-checking code in their software to detect and report them. Unfortunately, implementing checks with reporting capabilities that give the most useful results comes at a price. Such capabilities should report the exact nature of impending failures and additionally limit reporting to only the first occurrence of an error to prevent flooding the error log with the same message. They must report when an existing error or fault is replaced by another error of a different nature or value. They must recognize what makes occasional faults allowable and they must reset themselves upon recovery from a reported failure so the checking process can begin anew. They must also report recovery from previously reported failures that appear to have healed themselves. Since the price associated with providing all these features is limited by budget and schedule, system reliability and health monitoring often suffer. However, there are practical techniques that can simplify the effort associated with incorporating such error detection and reporting. When done properly, they can greatly improve system reliability and health monitoring by finding potentially hidden problems during development and can also greatly improve system maintainability by providing concise running descriptions of problems when things go wrong particularly when minor errors might otherwise go unnoticed. In addition, preventative maintenance can be greatly aided by applying error detection techniques to performance monitoring in the absence of errors. Many of the techniques described in this paper take advantage of simple classes to do bookkeeping tasks such as updating and tracking statistical analysis of errors and error reporting. The paper highlights several of these classes and gives examples from actual applications. View full abstract»

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  • Particle filter based prognosis study for predicting remaining useful life of steam generator tubing

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (411 KB) |  | HTML iconHTML  

    Steam Generator (SG) tubes are used to transfer heat from the primary side to pressurized steam on the secondary side in a nuclear power plant. Eddy current inspection of SG tubes is performed periodically and the data is analyzed to detect and characterize a variety of flaw categories in different locations of the SG tube geometry. This paper uses the data analysis results to estimate remaining useful life (RUL) of SG tubes in the nuclear reactor. Knowledge of RUL will ensure corrective action before the occurrence of tube leakage which may lead to an accident. A Monte Carlo based recursive algorithm known as particle filter is used to predict the RUL of the steam generator tubes. The uncertainty involved in prediction of remaining useful life of tubes using particle filter algorithm is controlled by an RUL correction loop based on an auto-regressive algorithm. The technique is applied to actual eddy current inspection data of steam generator tubing acquired at successive intervals to calculate RUL. The predicted RUL is compared to the actual failure time of the tube to show the efficacy of the technique. View full abstract»

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  • Extended Kalman Filter models and resistance spectroscopy for prognostication and health monitoring of leadfree electronics under vibration

    Publication Year: 2011 , Page(s): 1 - 12
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1369 KB) |  | HTML iconHTML  

    A technique has been developed for monitoring the structural damage accrued in BGA interconnects during operation in vibration environments. The technique uses resistance spectroscopy based state space vectors, rate of change of the state variable, and acceleration of the state variable in conjunction with Extended Kalman Filter and is intended for the pre-failure time-history of the component. Condition monitoring using the presented technique can provide knowledge of impending failure in high reliability applications where the risks associated with loss-of-functionality are too high to bear. The methodology has been demonstrated on SAC305 leadfree area-array electronic assemblies subjected to vibration. Future state of the system has been estimated based on a second order Extended Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying interconnect damage using plastic strain. Performance of the prognostication health management algorithm during the vibration test has been quantified using performance evaluation metrics. Model predictions have been correlated with experimental data. The presented approach is applicable to functional systems where corner interconnects in area-array packages may be often redundant. Prognostic metrics including α-λ metric, beta, and relative accuracy have been used to assess the performance of the damage proxies. The presented approach enables the estimation of residual life based on level of risk averseness. View full abstract»

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  • Decorrelated feature space and neural nets based framework for failure modes clustering in electronics subjected to mechanical-shock

    Publication Year: 2011 , Page(s): 1 - 16
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2530 KB) |  | HTML iconHTML  

    Electronic systems under extreme shock and vibration environments including shock and vibration may sustain several failure modes simultaneously. Previous experience of the authors indicates that the dominant failure modes experienced by packages in a drop and shock frame work are in the solder interconnects including cracks at the package and the board interface, pad cratering, copper trace fatigue, and bulk-failure in the solder joint. In this paper, a method has been presented for failure mode classification using a combination of Karhunen Loeve transform with parity-based stepwise supervised training of a perceptrons. Early classification of multiple failure modes in the pre-failure space using supervised neural networks in conjunction with Karhunen Loeve transform is new. Feature space has been formed by joint time frequency analysis. Since the cumulative damage may be accrued under repetitive loading with exposure to multiple shock events, the area array assemblies have been exposed to shock and feature vectors constructed to track damage initiation and progression. Error Back propagation learning algorithm has been used for stepwise parity of each particular failure mode. The classified failure modes and failure regions belonging to each particular failure modes in the feature space are also validated by simulation of the designed neural network used for parity of feature space. Statistical similarity and validation of different classified dominant failure modes is performed by multivariate analysis of variance and Hoteling's T-square. The results of different classified dominant failure modes are also correlated with the experimental cross sections of the failed test assemblies. The methodology adopted in this paper can perform real-time fault monitoring with identification of specific dominant failure mode and is scalable to system level reliability. View full abstract»

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  • The useful synergies between prognostics and HALT and HASS

    Publication Year: 2011 , Page(s): 1 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (677 KB) |  | HTML iconHTML  

    In this paper, we will explore the synergies between the technique Prognostics and the techniques HALT and HASS. We will explore: 1) Using HALT to accelerate a prognostics experiment. 2) Using HALT to help choose the appropriate stress levels for a Prognostic type of ALT. 3) Using prognostics during Manufacturing Screening help prove a HASS profile does not wear out products. 4) Using prognostics as an effective method of ESS. View full abstract»

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  • Diagnosis of multi-descriptor condition monitoring data

    Publication Year: 2011 , Page(s): 1 - 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3046 KB) |  | HTML iconHTML  

    Condition of equipment can be presented by a series of descriptors derived from the raw data. Typically a great number of descriptors are needed and they might not be commensurable. Neural networks can effectively be used as a diagnostic tool to analyze the data for anomalies and known faults. Proper pre processing of descriptors related to a specific machine condition offer an opportunity to automatically learn typical failure patterns and use this experience to diagnose any similar conditions in other machines operating in comparable environments. It is important to understand that the descriptors not only contain information on the type of the fault, but on the severity as well. Therefore the prognosis of failure severity can be based on the experimental data instead of an imprecise statistical approach. This paper presents several patented solutions for automating the diagnostic and prognostic processes using neural networks. View full abstract»

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  • Development and evaluation of AE based condition indicators for full ceramic bearing fault diagnosis

    Publication Year: 2011 , Page(s): 1 - 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1483 KB) |  | HTML iconHTML  

    Full ceramic bearings are considered the first step towards full ceramic, oil free engines in the future. No research on full ceramic bearing fault diagnostics using acoustic emission (AE) sensors has been reported. Unlike their steel counterparts, AE fault characteristic features of full ceramic bearings and effective signal processing methods for extracting these characteristic features have not been established. In this paper, the development and evaluation of AE based condition indicators (CIs) for full ceramic bearing fault detection and diagnosis are presented. The AE based CIs are developed using an empirical mode decomposition (EMD) based AE feature extraction method and evaluated by testing bearings with seeded outer race, inner race, ball, and cage faults on a bearing diagnostic test rig. View full abstract»

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  • A new vibration signal processing method for gearbox fault detection

    Publication Year: 2011 , Page(s): 1 - 5
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1288 KB) |  | HTML iconHTML  

    In this paper, a new vibration signal processing method, an adaptive narrow-band interference cancellation is developed to remove the periodic signals and background noises from the vibration signals. Narrow-band interference cancellation techniques are widely applied in signal processing of communication systems to remove the narrow-band interferences. The vibration signals of a gearbox with a damaged gear tooth contain periodic signals of both the healthy gear teeth and shaft. The periodic signals could be treated as narrowband interferences to the impulse signals of the damaged gear tooth and the background noises. The impulse signals are usually buried in the interference signals and background noises. To reveal the impulse signals for gear fault detection, the periodic signals and the noises should be removed. To cope with the dynamic interference of the vibration signals, an adaptive algorithm is incorporated in the narrow-band interference cancellation techniques. A case study with seeded gear fault tests is used to show the effectiveness of the developed method. View full abstract»

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  • Time-Sliced Temporal Evidential Networks: The case of Evidential HMM with application to dynamical system analysis

    Publication Year: 2011 , Page(s): 1 - 10
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (466 KB) |  | HTML iconHTML  

    Diagnostics and prognostics of health states are important activities in the maintenance process strategy of dynamical systems. Many approaches have been developed for this purpose and we particularly focus on data-driven methods which are increasingly applied due to the availability of various cheap sensors. Most data-driven methods proposed in the literature rely on probability density estimation. However, when the training data are limited, the estimated parameters are no longer reliable. This is particularly true for data in faulty states which are generally expensive and difficult to obtain. In order to solve this problem, we propose to use the theory of belief functions as described by Dempster, Shafer (Theory of Evidence) and Smets (Transferable Belief Model). A few methods based on belief functions have been proposed for diagnostics and prognostics of dynamical systems. Among these methods, Evidential Hidden Markov Models (EvHMM) seems promising and extends usual HMM to belief functions. Inference tools in EvHMM have already been developed, but parameter training has not fully been considered until now or only with strong assumptions. In this paper, we propose to complete the generalization of HMM to belief functions with a method for automatic parameter training. The generalization of this training procedure to more general Time-Sliced Temporal Evidential Network (TSTEN) is discussed paving the way for a further generalization of Dynamic Bayesian Network to belief functions with potential applications to diagnostics and prognostics. An application to time series classification is proposed. View full abstract»

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  • Automated scheduling using Condition Based Maintenance

    Publication Year: 2011 , Page(s): 1 - 8
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (755 KB) |  | HTML iconHTML  

    Maintenance scheduling of machines and their various components can be logistically challenging for a firm or factory. Traditional maintenance schedules are produced using time based preventive maintenance guidelines. But the preventive maintenance is imprecise as a machine might need a repair before the scheduled timeline due to over-use or premature failure of certain parts. Preventive Maintenance also may cause unneeded repair of parts that still have remaining useful life. Condition Based Maintenance (CBM) is a proactive maintenance approach that takes into account the real conditions of the parts using sensors and then offers guidelines to predict the functional failure ahead. The automated scheduling model that we describe here takes the CBM input into account along with the preventive maintenance guidelines, availability of parts, facilities and numerous other constraints to come up with optimum maintenance schedule. The automated scheduler is developed using Gecode based on Finite Domain Constraint paradigm that can take multiple constraints to model the various eccentricities of the scheduling problem. Other logistical support systems (e.g., ordering of parts, machinists etc) can also be scheduled using the Automated Scheduler alongside with scheduling maintenance of a machine. Since the scheduler can be run anytime, the most deserving candidate will be selected for maintenance at a given time. This will improve efficiency and reduce support cost by repairing the machine that is of urgent need. View full abstract»

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  • Essential steps in prognostic health management

    Publication Year: 2011 , Page(s): 1 - 9
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (640 KB) |  | HTML iconHTML  

    Prognostic health management (PHM) systems are designed to predict impending faults and to determine remaining useful life of machinery. An efficient prognostic system can speed up fault diagnosis by providing an indication of what parts of the machinery or vehicle are most likely to fail and will need maintenance in the near future. In this paper, we discuss the essential steps involved in building an effective PHM system. We describe time and frequency domain features that can be extracted from raw sensor data. These features or condition indicators can help summarize the information in raw data and extract critical clues that reflect the health of the machinery. Analytical models can then be used to learn the essential health indicators and how they relate to fault conditions. In addition, we describe a case study of implementing a PHM system for a high speed face milling CNC cutter. We describe features that were analyzed from sensor data. For the analytical engine, we used a Neural Network model for learning the association of the extracted features and the magnitude of wear in the cutter. The neural network was able to determine remaining useful life of cutters in terms of number of remaining cuts for a given wear limit based on extracted features. View full abstract»

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  • Continuous health condition monitoring: A single Hidden Semi-Markov Model approach

    Publication Year: 2011 , Page(s): 1 - 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1374 KB) |  | HTML iconHTML  

    In this paper, a single Hidden Semi-Markov Model (HSMM) approach is introduced for continuous health condition monitoring in machinery systems. Contrary to previous attempts in using hidden Markov models in this area which have not provided the relationship between the hidden state values and the physical states, this method provides the aforementioned relationship. In this paper, HSMM is applied as the core model being used in the method in order to increase flexibility of our previously used HMM-based method and consequently its generalization capability. The newly introduced method is compared with our initial HMM-based method which previously outperformed the conventional Artificial Neural Networks approach. Results show that the additional flexibility provided in the new method has improved the performance. As an example, the proposed method is used for tool wear prediction in a CNC-milling machine and results of the study is provided. 482 features are extracted from 7 signals (three force signals, three vibration signals and Acoustic Emission) acquired for each experiment of our dataset. These features include, 48 statistical features extracted from force signals in three directions (16 from each force signal) and 434 averaged wavelet coefficients from all seven signals (62 from each signal). After feature extraction phase, Fisher Discriminant Ratio is applied to find the most discriminant features to construct the prediction model. 38 features out of 482 extracted features are selected to be used in the prediction models. The prediction results are provided for three different cases i.e. cross-validation, diagnostics and prognostics. View full abstract»

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  • A prognostic framework for health management of coupled systems

    Publication Year: 2011 , Page(s): 1 - 10
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (512 KB) |  | HTML iconHTML  

    This paper describes a unified data-driven prognostic framework that combines failure time data, static parameter data and dynamic (time-series) data. The approach employs Cox proportional hazards model (Cox PHM) and soft dynamic multiple fault diagnosis algorithm (DMFD) for inferring the degraded state trajectories of components and to estimate their remaining useful life (RUL). This framework takes into account the cross-subsystem fault propagation, a case prevalent in any networked and embedded system. The key idea is to use Cox proportional hazards model to estimate the survival functions of error codes and symptoms (soft test outcomes/prognostic indicators) from failure time data and static parameter data, and use them to infer the survival functions of components via a soft DMFD algorithm. The average remaining useful life and its higher-order central moments (e.g., variance, skewness, kurtosis) can be estimated from these component survival functions. The proposed prognostic framework has the potential to be applicable to a wide variety of systems, ranging from automobiles to aerospace systems. View full abstract»

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  • Developing effective continuous on-line monitoring technologies to manage service degradation of nuclear power plants

    Publication Year: 2011 , Page(s): 1 - 7
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (699 KB) |  | HTML iconHTML  

    Recently, there has been increased interest in using prognostics (i.e, remaining useful life (RUL) prediction) for managing and mitigating aging effects in service-degraded passive nuclear power reactor components. A vital part of this philosophy is the development of tools for detecting and monitoring service-induced degradation. Experience with in-service degradation has shown that rapidly-growing cracks, including several varieties of stress corrosion cracks (SCCs), can grow through a pipe in less than one fuel outage cycle after they initiate. Periodic inspection has limited effectiveness at detecting and managing such degradation requiring a more versatile monitoring philosophy. Acoustic emission testing (AET) and guided wave ultrasonic testing (GUT) are related technologies with potential for on-line monitoring applications. However, harsh operating conditions within NPPs inhibit the widespread implementation of both technologies. For AET, another hurdle is the attenuation of passive degradation signals as they travel though large components, relegating AET to targeted applications. GUT is further hindered by the complexity of GUT signatures limiting its application to the inspection of simple components. The development of sensors that are robust and inexpensive is key to expanding the use of AET and GUT for degradation monitoring in NPPs and improving overall effectiveness. Meanwhile, the effectiveness of AET and GUT in NPPs can be enhanced through thoughtful application of tandem AET-GUT techniques. View full abstract»

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  • Effects of laser peening on fatigue life in an arrestment hook shank application for naval aircraft

    Publication Year: 2011 , Page(s): 1 - 11
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3189 KB) |  | HTML iconHTML  

    Laser peening is evaluated relative to and in combination with other means of improving fatigue resistance in a particularly severe arrestment hook shank application for a carrier-based Naval aircraft. A large-scale test specimen was designed and manufactured from Hy-Tuf steel to geometrically simulate conditions in the arrestment hook shank, and fatigue tests were conducted on peened specimens under conditions of spectrum loading that simulate aircraft arrestments. Laser peening substantially increases the resistance to crack initiation relative to conventional shot peening. A change to a higher-strength steel, Ferrium S53, significantly increases the crack initiation life of both conventionally shot peened and laser peened specimens, with the latter exhibiting the highest levels of crack initiation life for all conditions evaluated in this test program. Proof loading at levels above the design limit load, conducted before the peening operation, substantially increases the crack initiation life of conventionally shot peened specimens. In contrast, proof loading does not provide additional improvements in the fatigue life of laser peened specimens since the depth of plasticity due to proof loading is similar in magnitude to the depth of residual compressive stress introduced by laser peening. Finally, limited test results suggest that repeated laser peening over the life of a component may maximize or even extend crack initiation life; however, a significant amount of testing would be required to determine the processing parameters that maximize the efficacy of this approach to extending fatigue life. View full abstract»

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  • Improved rotor track and balance performance using an expert system

    Publication Year: 2011 , Page(s): 1 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (642 KB) |  | HTML iconHTML  

    A major source of vibration in the helicopter is derived from the main rotor dynamics and imbalance. Excessive vibration can cause premature wear to the aircraft and rotating components. Additionally, vibration causes pilot fatigue that in itself can be a safety issue. While great improvements have been made in the rotor track and balance (RTB) techniques, it still requires a skilled maintainer. Systems such as the Goodrich IVHMU can give good solutions. However, it requires a high degree of user expertise in selecting which RTB control adjustments should be active for the current helicopter measured vibration. Selection of RTB control adjustments or solution strategies is based on the type of imbalance/vibrations that are being generated by the main rotor. A system which recognizes the current vibration state can be trained to use the best controls and solution strategy. Such an expert system would improve the quality of the RTB solution and in general reduce the overall workload of the maintainer. We present a framework for a Bayesian classifier expert system. The classifier is based on a multiple hypothesis testing and demonstrates the ability to deliver an improved RTB adjustment over a generalized solution strategy. View full abstract»

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  • Bearing envelope analysis window selection Using spectral kurtosis techniques

    Publication Year: 2011 , Page(s): 1 - 6
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (801 KB) |  | HTML iconHTML  

    Envelope Analysis is a well-known signal processing technique for bearing fault detection. However, improper window selection can result in poor fault detection performance. Using a known fault data set, we quantify the performance of spectral kurtosis (SK) and envelope kurtosis (EK) as a technique for setting an optimal frequency and bandwidth window for the envelope analysis. We establish a measure of effectiveness (MOE): the correlation of fault energy with total spall length. With this MOE, we evaluate the ability of SK/EK to predict the optimal envelope analysis window. View full abstract»

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  • Methodology and framework for predicting rolling element helicopter bearing failure

    Publication Year: 2011 , Page(s): 1 - 9
    Cited by:  Papers (4)
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    The enhanced ability to predict the remaining useful life of helicopter drive train components offers potential improvement with regards to safety, maintainability, and reliability of a helicopter fleet. Current existing helicopter health and usage monitoring systems provide diagnostic information that indicates when the condition of a drive train component is degraded; however, prediction techniques are not currently used. Although various algorithms exist for providing remaining life predictions, considering the limited number of run-to-failure data sets, the maturation of the prognostic techniques has not been achieved. This particular study addresses remaining useful life predictions for the helicopter oil-cooler bearing. The paper proposes a general methodology of how to perform rolling element bearing prognostics and presents the results using a robust regression curve fitting approach. The proposed methodology includes a series of processing steps prior to the prediction routine, including feature extraction, feature selection, and health assessment. This provides a framework for including prediction algorithms into existing health and usage monitoring systems. An oil-cooler bearing test-rig constructed by Impact Technologies LLC is used to facilitate the development of the remaining life prediction techniques. Two data sets are used in this study, in which both bearings experienced an inner race spall that progressed until the test was stopped due to an unsafe vibration level. The robust regression curve fitting results are promising in that the actual and predicted remaining life estimates converge for the run-to-failure oil-cooler bearing data sets a few hours prior to the stopping of the test. Future work would consider using the same methodology but comparing the accuracy of this prediction method with Bayesian filtering techniques, usage based methods, and other time series prediction methods. View full abstract»

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