Current Publications – ICSL

Current Publications – ICSL

Fault Diagnosis and Failure Prognosis for Engineering Systems

Journal Papers

2009

Abstract—This paper introduces an on-line particle-filtering (PF)-based framework for fault diagnosis and
failure prognosis in non-linear, non-Gaussian systems. This framework considers the implementation
of two autonomous modules. A fault detection and identification (FDI) module uses
a hybrid state-space model of the plant and a PF algorithm to estimate the state probability
density function (pdf) of the system and calculates the probability of a fault condition in realtime.
Once the anomalous condition is detected, the available state pdf estimates are used as
initial conditions in prognostic routines. The failure prognostic module, on the other hand,
predicts the evolution in time of the fault indicator and computes the pdf of the remaining
useful life (RUL) of the faulty subsystem, using a non-linear state-space model (with unknown
time-varying parameters) and a PF algorithm that updates the current state estimate. The
outcome of the prognosis module provides information about the precision and accuracy of
long-term predictions, RUL expectations and 95% confidence intervals for the condition under
study. Data from a seeded fault test for a UH-60 planetary gear plate are used to validate the
proposed approach.

Abstract—Fault diagnosis and failure prognosis are essential
techniques in improving the safety of many mechanical systems.
However, vibration signals are often corrupted by noise; therefore,
the performance of diagnostic and prognostic algorithms is
degraded. In this paper, a novel denoising structure is proposed
and applied to vibration signals collected from a testbed of the
helicopter main gearbox subjected to a seeded fault. The proposed
structure integrates a denoising algorithm, feature extraction, failure
prognosis, and vibration modeling into a synergistic system.
Performance indexes associated with the quality of the extracted
features and failure prognosis are addressed, before and after
denoising, for validation purposes.

Abstract— The tum-to-tum short is one major fault of the motor faults of BLDC motors and can appear frequently. When the fault happens, the motor can be operated without breakdown, but it is necessary to maintain the motor for continuous working. In past research, several methods have been applied to detect winding faults. The representative approaches have been focusing on current signals, which can give important information to extract features and to detect faults. In this study, current sensors were installed to measure signals for fault detection of BLDC motors. In this study, the Park’s vector method was used to extract the features and to isolate the faults from the current measured by sensors. Because this method can consider the three-phase current values, it is useful to detect features from one-phase and three-phase faults. After extracting two-dimensional features, the final feature was generated by using the two-dimensional values using the distance equation. The values were used in fuzzy similarity to isolate the faults. Fuzzy similarity is an available tool to diagnose the fault without model generation and the fault was converted to the percentage value that can be considered as possibility of the fault.

2008

Abstract—This paper introduces an on-line particle-filtering-based framework for fault diagnosis and failure prognosis in nonlinear, non-Gaussian systems. This framework considers the implementation of two autonomous modules. A fault detection and identification (FDI) module uses a hybrid state-space model of the plant and a particle-filtering (PF) algorithm to estimate the state probability density function (pdf) of the system and calculates the probability of a fault condition in real-time. Once the anomalous condition is detected, the available state pdf estimates are used as initial conditions in prognostic routines. The failure prognostic module, on the other hand, predicts the evolution in time of the fault indicator and computes the pdf of the remaining useful life (RUL) of the faulty subsystem, using a nonlinear state-space model (with unknown time-varying parameters) and a particle-filtering algorithm that updates the current state estimate. The outcome of the prognosis module provides information about the precision and accuracy of long-term predictions, RUL expectations, and 95% confidence intervals for the condition under study. Data from a seeded fault test for a UH-60 planetary gear plate are used to validate the proposed approach.

Abstract—Fault diagnosis and failure prognosis are essential techniques in improving the safety of many mechanical systems. However, vibration signals are often corrupted by noise; therefore, the performance of diagnostic and prognostic algorithms is degraded. In this paper, a novel denoising structure is proposed and applied to vibration signals collected from a testbed of the helicopter main gearbox subjected to a seeded fault. The proposed structure integrates a denoising algorithm, feature extraction, failure prognosis, and vibration modeling into a synergistic system. Performance indexes associated with the quality of the extracted features and failure prognosis are addressed, before and after denoising, for validation purposes.

  • Ginart, A., Brown, D., Kalgren, P., Roemer, M., “On-line Ringing Characterization as PHM Technique for Power Drive and Electrical Machinery,” in press, to be published in IEEE Transactions on Instrumentation & Measurement, 2008.

Abstract—Embeddable features easily incorporated in traditional power drive systems are identified for prognostic health management (PHM) systems. This novel technique utilizes the original PWM waveform produced by the inverter to evaluate the power electronic circuit and electric machine against transistor degradation. Evaluation of the primary feature, ringing characterization, with experimental data demonstrates its viability as a practical real-time power device health-state indicator.

Abstract—Critical aircraft assets are required to be available when needed, while exhibiting attributes of reliability, robustness and high confidence under a variety of flight regimes, and maintained on the basis of their current condition rather than on the basis of scheduled maintenance practices. New and innovative technologies must be developed and implemented to address these concerns. Condition Based Maintenance (CBM) requires that the health of critical components/systems be monitored and diagnostic/prognostic strategies be developed to detect and identify incipient failures and predict the failing component’s remaining useful life (RUL). Typically, vibration and other key indicators on-board an aircraft are severely corrupted by noise thus curtailing the ability to accurately diagnose and predict failures. This paper introduces a novel blind deconvolution de-noising scheme that employs a vibration model in the frequency domain and attempts to arrive at the true vibration signal through an iterative optimization process. Performance indexes are defined and data from a helicopter are used to demonstrate the effectiveness of the proposed approach.

Abstract-With increased system complexity, Condition-Based Maintenance (CBM) becomes a promising solution to system safety by detecting faults and scheduling maintenance procedures before faults become severe failures resulting in catastrophic events. For CBM of many mechanical systems, fault diagnosis and failure prognosis based on vibration signal analysis are essential techniques. Noise originating from various sources, however, often corrupts vibration signals and degrades the performance of diagnostic and prognostic routines, and consequently, the performance of CBM. In this paper, a new de-noising structure is proposed and applied to vibration signals collected from a testbed of the main gearbox of a helicopter subjected to a seeded fault. The proposed structure integrates a blind deconvolution algorithm, feature extraction, failure prognosis, and vibration modeling into a synergistic system, in which the blind deconvolution algorithm attempts to arrive at the true vibration signal through an iterative optimization process. Performance indexes associated with quality of the extracted features and failure prognosis are addressed, before and after de-noising, for validation purposes.

Conference Papers

2009

Abstract—A complex system such as an aircraft engine, is
composed of several components or subsystems which
interact with each other in several ways. These constituent
components/subsystems and their interactions make up the
whole system. When a fault condition arises in one of the
components, not only that component behavior changes but
the interaction of that component with the other system
constituents may also change. This might result in spreading
the effect of that fault to other components in a domino like
effect, until the overall system fails. This paper presents a
modular methodology to analyze propagation of faults from
one subsystem to the other subsystems. The application
domain focuses on an aero propulsion system of the
turbofan type.

Abstract—Complex engineering systems consist of many
subsystems. Each of the subsystems is composed
of a large number of components. While faults
arise at component level, sensing capabilities are
limited to subsystem level, and system operations
and maintenance practices are scheduled based
on system level paremeters. This paper presents
a hierarchical architecture to analyze the effects
of system level parameters on component level
faults of dominant failure modes of a complex
system. An aeropropulsion system of turbofan
type has been used as the application domain. In
most of the cases, engine life is limited due to
cracks in high-pressure turbine blades. In this paper,
it is assumed that creep is the only active failure
mechanism. Based on a finite-element model
of the turbine blades available in the open literature,
design of experiments (DoE) methodology
is used to build a subsystem-level model. A simulation
package of a commercial aircraft engine
is then used to obtain system-level results.

Abstract—In complex systems, there are few critical failure
modes. Prognostic models are focused at predicting the
evolution of those critical faults, assuming that other
subsystems in the same system are performing according to
their design specifications. In practice, however, all the
subsystems are undergoing deterioration that might accelerate
the time evolution of the critical fault mode. This paper aims at
analyzing this aspect, i.e. interaction between different fault
modes in various subsystems, of the failure prognostic
problem. The application domain focuses on an aero
propulsion system of the turbofan type. Creep in the highpressure
turbine blade is one of the most critical failure modes
of aircraft engines. The effects of health deterioration of lowpressure
compressor and high–pressure compressor on creep
damage of high-pressure turbine blades are investigated and
modeled.

  • Orchard, M., Tang, L., Goebel, K., and Vachtsevanos, G., “A Novel RSPF Approach to Prediction of High-Risk, Low-Probability Failure Events,” First Annual Conference of the Prognostics and Health Management Society – PHM09, 2009, San Diego, CA, USA.
  • Brown, D., Georgoulas, G., Bole, B., Pei, H.L, Orchard, M., Tang, L., Saha, B., Saxena, A., Goebel, K., and Vachtsevanos, G., “Prognostics Enhanced Reconfigurable Control of Electro-Mechanical Actuators,” First Annual Conference of the Prognostics and Health Management Society – PHM09, 2009, San Diego, CA, USA.

2008

  • Abbas, M., Vachtsevanos, G., “A Fault Propagation Analysis Methodology Based on Energy Flow,” International Conference on Prognostics and Health Management 2008.

Abstract—Particle filters (PF) have been established as the de facto state of the art in failure prognosis. They combine advantages of the rigors of Bayesian estimation to nonlinear prediction while also providing uncertainty estimates with a given solution. Within the context of particle filters, this paper introduces several novel methods for uncertainty representations and uncertainty management. The prediction uncertainty is modeled via a rescaled Epanechnikov kernel and is assisted with resampling techniques and regularization algorithms. Uncertainty management is accomplished through parametric adjustments in a feedback correction loop of the state model and its noise distributions. The correction loop provides the mechanism to incorporate information that can improve solution accuracy and reduce uncertainty bounds. In addition, this approach results in reduction in computational burden. The scheme is illustrated with real vibration feature data from a fatigue-driven fault in a critical aircraft component.

Abstract—This paper introduces a novel Prognostics-enhanced Automated Contingency Management (or ACM+P) paradigm based on both current health state (diagnosis) and future health state estimates (prognosis) for advanced autonomous systems. Including prognostics in ACM system allows not only fault accommodation, but also fault mitigation via proper control actions based on short term prognosis, and moreover, the establishment of a long term operational plan that optimizes the utility of the entire system based on long term prognostics. Technical challenges are identified and addressed by a hierarchical ACM+P architecture that allows fault accommodation and mitigation at various levels in the system ranging from component level control reconfiguration, system level control reconfiguration, to high level mission re-planning and resource redistribution. The ACM+P paradigm was developed and evaluated in a high fidelity Unmanned Aerial Vehicle (UAV) simulation environment with flight-proven baseline flight controller and simulated diagnostics and prognostics of flight control actuators. Simulation results are presented. The ACM+P concept, architecture and the generic methodologies presented in this paper are applicable to many advanced autonomous systems such as deep space probes, unmanned autonomous vehicles, and military and commercial aircrafts.

Abstract-This endeavor introduces a novel methodology for the early detection of anomalies or faults with confidence in critical aircraft systems. The anomaly detector is based on particle filtering estimation techniques and is intended to recognize deviations from the nominal operation of a system or component. An appropriate baseline is defined first from available historical data and is expressed as a probability density function in the particle filtering framework. Real-time vibration signals and information regarding the system’s operational state are made available on-line and pre-processed to de-noise the data and extract useful features or condition indicators. Statistical analysis is performed on these features and the latter are compared, as they progress in time, with baseline statistics. The algorithm determines the probability of anomalous conditions, the probability of false alarms, and an indicator that reports the time instant of the presence of a fault or anomaly for a specified confidence level. A case study with actual experimental data derived from seeded fault testing is used to demonstrate the efficacy of the proposed approach.

Abstract—This paper introduces a methodology to detect as early as possible with specified degree of confidence and prescribed false alarm rate an anomaly or novelty (incipient failure) associated with critical components/subsystems of an engineered system that is configured to monitor continuously its health status. Innovative features of the enabling technologies include a Bayesian estimation framework, called particle filtering, that employs features or condition indicators derived from sensor data in combination with simple models of the system’s degrading state to detect a deviation or discrepancy between a baseline (no-fault) distribution and its current counterpart. The scheme provides the probability of abnormal condition and the probability of false alarm. The presence of an anomaly is confirmed for a given confidence level. The efficacy of the proposed anomaly detection architecture is illustrated with test data acquired from components typically found on aircraft and monitored via a test rig appropriately instrumented.

Abstract— Resolver sensors are utilized as absolute position transducers to control the position and speed of actuators in many flight critical applications where robustness accuracy and ability to operate in extreme environmental conditions are required. To ensure these requirements, several designs for self-diagnosing sensors were proposed in the past. However, such designs require modifications to the transducer itself. This paper proposes a new approach for real-time tracking of resolver faults with the ability to perform fault accommodation in the event of a fault. A formation based on the physical operating principles for a resolver sensor is provided. Diagnostic measures are identified for use in statistical-based fault-detection routines. Then, estimates for the resolver mismatch are tracked using a time-varying Kalman filter. Fault accommodation is achieved by applying the tracked estimates to adjust for the resolver mismatch. Finally, the fault detection and accommodation routines were evaluated in Simulink for an electro-mechanical actuator (EMA).

Abstract—In this paper, an anomaly detection structure, in which different types of anomaly detection routines can be applied, is proposed. Bearing fault modes and their effects on the bearing vibration are discussed. Based on this, a feature extraction method is developed to overcome the limitation of time domain features. Experimental data from bearings under different operating conditions are used to verify the proposed method. The results show that the extracted feature has a monotonic decrease trend as the dimension of fault increases. The feature also has the ability to compensate the variation of rotating speed. The proposed structure are verified with three different detection routines, pdf-based, k-nearest neighbor, and particle-filter-based approaches.