This paper examines the problem of identifying cracks in planetary gear systems through the use of vibration sensors on the stationary gearbox housing. In particular, the effect of unequal spacing of planet gears relative to the rotating carrier plate on various frequency components in the vibration spectra is studied. The mathematical analysis is validated with experimental data comparing the vibration signature of helicopter transmissions operating either normally or with damage, leading to shifts in the planet gear positions. The theory presented is able to explain certain features and trends in the measured vibration signals of healthy and faulty transmissions. The characterization offered may serve as a means of detecting damage in planetary gear systems.

This paper introduces a method to detect a fault associated with critical components/subsystems of an engineered system. It is required, in this case, to detect the fault condition as early as possible, with specified degree of confidence and a prescribed false alarm rate. Innovative features of the enabling technologies include a Bayesian estimation algorithm called particle filtering, which 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 requires a fault progression model describing the degrading state of the system in the operation. A generic model based on fatigue analysis is provided and its parameters adaptation is discussed in detail. The scheme provides the probability of abnormal condition and the presence of a fault is confirmed for a given confidence level. The efficacy of the proposed approach is illustrated with data acquired from bearings typically found on aircraft and monitored via a properly instrumented test rig.

Lubricant condition monitoring is widely recognized as an effective means to determine the health of gearboxes and drive train components. For example, in rotorcraft systems, where the health of drive train and engine systems is paramount for safe operation and readiness, a staple means of condition monitoring of legacy gearboxes has been the use of both offline oil analysis and chip detection sensors, primarily because of their effectiveness in detecting both potential and actual sources of mechanical degradation in the oil. However, the challenges associated with these types of separate monitoring activities, including the high cost and complex logistical burden laid upon systems or aircraft in service, and their information and detection timing limitations, make an online monitoring technology for gearbox lubricant condition and debris detection highly desirable. To this end, Impact Technologies, LLC is developing an integrated online monitoring system capable of both real-time oil condition monitoring as well as ferrous and non-ferrous metallic debris detection. This innovative system integrates two separate technologies into a single device to better meet performance demands for lubricant-based health monitoring of high performance gearboxes and drive trains. This paper describes the benefits of the combined technologies and outlines efforts and lessons learned during the design process to achieve high fidelity and reliability in a tightly packaged, lightweight and robust mechanical and electronic design resistant to vibration and thermal exposure. The paper also discusses key monitoring capabilities as well as system validation, testing and performance characterization for such relevant technology.

Although typical Health and Usage Monitoring Systems (HUMS) intend to support a transition from scheduled part replacements to performing maintenance upon evidence of need, they generally exhibit a limited ability to diagnose component faults early and accurately in complex systems such as a helicopter drive train. Consequently, the traditional approach to implementing Condition Based Maintenance (CBM) programs is slow, requires substantial amounts of human supervision, and ultimately lacks prognostics. A team of developers from Impact Technologies, the U.S. Army Research Laboratory and the Georgia Institute of Technology, with support from the U.S. Army, have been working over the past 3 years to develop a methodology to improve the performance of U.S. Army helicopter CBM systems, and facilitate transition from scheduled maintenance to implementation of prognostics. This paper presents an integrated diagnostic enhancement and prognostic architecture and the corresponding software suite, and discusses how a hybrid and systematic approach to sensing, data processing, fault feature extraction, fault diagnosis, and parallel health-based and usage-based failure prognosis can be used to improve the performance of HUMS and CBM activities. The paper also provides an application example, and discusses validation steps for the new approach to diagnostic enhancement and prognostics.

The successful implementation of fault diagnosis and failure prognosis algorithms to safety critical systems requires the definitions and applications of mathematically rigorous modules. These modules, including data pre-processing, feature extraction, diagnostic and prognostic algorithms, performance metrics definition, and a fault progression model, form an integrated architecture for system health monitoring and management. In these modules, the fault progression model is critical to detection of incipient failures as early as possible with predefined specifications and prediction of the system's remaining useful life accurately and precisely. This paper considers an oil cooler bearing of a helicopter and proposes a methodology for fault detection and failure prognosis, in which data pre-processing, feature extraction and fault progression modeling are discussed in detail. Experimental results are presented to verify the proposed methodology and fault progression model.

Although typical Health and Usage Monitoring Systems (HUMS) intend to support a transition from scheduled part replacements to performing maintenance upon evidence of need, they generally exhibit a limited ability to diagnose component faults early and accurately in complex systems such as a helicopter drive train. Consequently, the traditional approach to implementing Condition Based Maintenance (CBM) programs is slow, requires substantial amounts of human supervision (including case-by-case data analysis and results verification), and ultimately shuns prognostic activities. Causes of these limitations, which ultimately lead to an underrepresentation of prognostics in fielded CBM systems, include: (i) the sensitivity of sensors and condition indicators to signal noise and operating modes; (ii) use of empirical condition indicators not fully understood at the fleet-wide level; (iii) uncertainty in damage progression tracking; (iv) the inherent risk of condition prognosis; and (v) the lack diagnostic and prognostic validation with known fault cases. To improve the performance of CBM systems and facilitate transition from scheduled maintenance to reliable implementation of diagnostics and prognostics, a team of developers from Impact Technologies, the U.S. Army Research Laboratory and the Georgia Institute of Technology, with support from the U.S. Army have been working over the past 21⁄2 years to develop a methodology that is capable of addressing the challenges listed. This work has been a part of the Air Vehicle Diagnostics and Prognostics Improvement Program (A VDPIP), a collaborative agreement to develop, test and evaluate modular software components that provide enhancements to diagnostic systems already in service, as well as add failure prognosis capabilities for critical Army aircraft components. This paper presents the integrated diagnostic enhancement and prognostic architecture, as well as the software suite developed under the collaborative program, and discusses how a hybrid and systematic approach to sensing, data processing, fault feature extraction, fault diagnosis, and parallel health- based and usage-based failure prognosis can be used to improve the performance of a wide variety of HUMS and CBM activities in support of implementing prognostics. The software architecture contains generic components and algorithms building on model based and data driven methodologies that are applicable to a variety of critical components in complex systems such as those found in a helicopter drive train

Fixed transducers often are used to monitor meshing gear pairs in order to detect tooth damage. A simple frequency-domain damage-detection algorithm is suggested for very early detection of such damage. Ratios of rotational-harmonic amplitudes computed from before and after potential damage are utilized to eliminate effects of transducer and structural-path-caused amplitude changes between tooth-meshing location and transducer output, to minimize attenuating effects of multiple-tooth contact, and thereby, to approximately equally weight rotational-harmonic amplitudes over a wide range of harmonics. Statistical averaging of absolute values of logarithmic ratios of rotational-harmonic amplitudes is used to minimize fluctuations caused by multiple-tooth contact and manufacturing errors on the subject gear. Synchronous averaging is employed to minimize effects of noise and manufacturing errors on the mating gear. Time-windowing tailored to contact ratios of mating gears is utilized to isolate individual tooth locations. Resultant windowing effects on availability of useful rotational harmonics are analyzed. Application of the algorithm to detection of seeded bending-fatigue faults on a planetary ring-gear tooth indicates that successful detections were achieved.

Engineering systems, such as aircraft, industrial processes, manufacturing systems, transportation systems, electrical and electronic systems, etc., are becoming more complex and are subjected to failure modes that impact adversely their reliability, availability, safety and maintainability. Such critical assets are required to be available when needed, and maintained on the basis of their current condition rather than on the basis of scheduled or breakdown maintenance practices. Moreover, on-line, real-time fault diagnosis and prognosis can assist the operator to avoid catastrophic events. Recent advances in Condition-Based Maintenance and Prognostics and Health Management (CBM/PHM) have prompted the development of new and innovative algorithms for fault, or incipient failure, diagnosis and failure prognosis aimed at improving the performance of critical systems. This paper introduces an integrated systemsbased framework (architecture) for diagnosis and prognosis that is generic and applicable to a variety of engineering systems. The enabling technologies are based on suitable health monitoring hardware and software, data processing methods that focus on extracting features or condition indicators from raw data via data mining and sensor fusion tools, accurate diagnostic and prognostic algorithms that borrow from Bayesian estimation theory, and specifically particle filtering, fatigue or degradation modeling, and real-time measurements to declare a fault with prescribed confidence and given false alarm rate while predicting accurately and precisely the remaining useful life of the failing component/system. Potential benefits to industry include reduced maintenance costs, improved equipment uptime and safety. The approach is illustrated with examples from the aircraft and industrial domains.

With increased system complexity, condition-based maintenance (CBM) becomes a promising solution for 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 modelling 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.

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.

A major objective of health and usage monitoring systems (HUMS) is to transition from time based part replacement to performing maintenance actions based on evidence of need. While existing HUMS capability has demonstrated progress, the ability to diagnose component faults in their early stages is limited. This is due in part to sensitivity to signal noise, variations in environmental and operating conditions, and underutilization of prognostic techniques. Using the representative example of the fan support bearing in the oil cooler of the UH-60 helicopter, this paper discusses key areas to improve fault detection methods for health monitoring of a damaged helicopter transmission component. These include: (1) sensing and data processing tools, (2) selection and extraction of optimum condition indicators/features, (3) fusion of data at the sensor and feature levels, and (4) incipient fault detection using a Bayesian estimation framework. Results illustrating the effectiveness of these techniques are presented for fielded UH-60 bearing vibration data and laboratory test results.

— Airborne electronic systems have been used virtually everywhere on board military and commercial aircraft. Since Field Effect Transistors (FETs) are building blocks for the electronic systems and their components, the diagnosis and prognosis of potential FET system failures are critical to the flight and ground crew. In this paper, we developed an advanced prognostic methodology based on the Direct Drain Quiescent Current (IDDQ) testing technique for potential Field Effect Transistors (FETs) failures. To predict the Remaining useful life (RUL) of the FET-based devices, a thorough failure mechanism study for FETs was performed in order to select a subset of failure mechanisms that cause progressive degradation and relate with IDDQ signals. With the selected failure mechanisms, we utilized the symbolic dynamics-based method to perform the fault degradation status estimation and a novel Uncertainty Adjusted Prognostics (UAP) method to predict the RUL with uncertainty management. Finally, the prognostic methodology was verified using developed 2-D/3-D simulation models.

This paper examines the problem of identifying cracks in planetary gear systems through use of vibration sensors on the stationary gearbox housing. In particular, the effect of unequal spacing of planet gears relative to the rotating carrier plate on various frequency components in the vibration spectra is studied. The mathematical analysis is validated with experimental data comparing the vibration signature of helicopter transmissions operating either normally or with damage leading to shifts in the planet gear positions. The theory presented is able to explain certain features and trends in the measured vibration signals of healthy and faulty transmissions. The characterization offered may serve as a means of detecting damage in planetary gear systems.

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 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. Typically, vibration and other key indicators onboard an aircraft are severely corrupted by noise, thus curtailing the ability to accurately diagnose and predict failures. This paper introduces a novel blind deconvolution denoising 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.

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 diagnostics/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 on aircraft are severely corrupted by noise thus curtailing our ability to accurately diagnose and predict failures. This paper introduces a novel blind deconvolution de-noising scheme that employs 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 scheme.

This paper introduces the design of an integrated framework for on-board fault diagnosis and failure prognosis of a helicopter transmission component, and describes briefly Its main modules. It suggests means to (1) validate statistically and pre-process sensor data (vibration), (2) integrate model-based diagnosis and prognosis, (3) extract useful features or condition indicators from data de-noised by blind deconvolution, and (4) combine Bayesian estimation algorithms and measurements to detect and identify the fault and predict remaining useful life with specified confidence and minimum false alarms.

In this paper, vibration data analysis techniques are investigated for fault diagnosis of helicopter planetary gears. A data pre-processing technique is introduced that achieves the same result as the commonly used Time Synchronous Averaging with much lower computational complexity since interpolation is not required. A notion of using raw vibration data instead of the Time Synchronous Averaged data is also presented that is more suitable for the analysis of vibration data produced by planetary gearboxes and for the purposes of detecting carrier plate crack fault. Based on this notion, features such as the Harmonic Index in the frequency domain and the Intra-Revolution Energy Variance in the wavelet domain are derived. The features are used as inputs to fault classifiers and are shown to detect the fault successfully based on the test data that is available.

Failure of flight critical components on-board a helicopter could cause an accident resulting in loss of life and/or aircraft. It is imperative, therefore, that precursors of such failure modes be monitored continuously and remedial action be taken as soon as feasible in order to avoid catastrophic events. A crack in the planetary carrier of a UH-60 Blackhawk main transmission has received recently extensive evaluation through analysis of vibration data (J. Keller et al., 2003). The rotorcraft main transmission includes a planetary gear train comprising an inner "sun" gear surrounded by five rotating "planets". Torque is transmitted through the sun gear to the planets, which ride on a planetary carrier. The planetary carrier plate, in turn, transmits torque to the main rotor shaft and blades. The U.S. Army Aviation Engineering Directorate conducted a series of experimental tests with faulted and unfaulted carrier plates on a test cell and also on-aircraft to determine if a fault (a plate crack) can be detected via vibration monitoring. This paper introduces a methodology to analyze raw vibration data provided to Georgia Tech from the carrier testing. The analysis approach consists of selection and extraction of appropriate features from vibration data indicative of the fault condition and the construction of an optimum feature vector. Test cell data sampled at 100 kHz for torque cases ranging from 20% to 100% were processed. On-aircraft data covered a limited torque range up to 30% due to safety considerations. Both raw and time synchronous data were considered and features in the time, frequency and wavelet domains were investigated. The analysis results indicate that a selected subset of features clearly distinguishes between the faulted and unfaulted cases.

Accurate and reliable fault diagnosis and prognosis of safety or mission critical components/ subsystems in complex engineering systems present major challenges to the Condition-Based Maintenance (CBM) or Prognostic and Health Management (PHM) designer. A crucial step in the development of CBM/PHM strategies relates to the designer’s ability to understand and model the incipient failure or fault modes and mechanisms. A single fault growth model might not be often capable to capture a sequence of fault behaviors. Consider, for example, a rolling element bearing as a critical component of rotating machinery. The bearing may begin to corrode under certain operating conditions and, in parallel or sequentially, may be spalling and eventually, cracking. For accurate model-based fault diagnosis and failure prognosis, therefore, it is essential that fault progression models be developed to represent these evolving behaviors. This paper introduces an approach to multi-fault modeling with an application to a rolling element bearing of a helicopter’s oil cooler. A simple and cost-effective on-line parameter adaptation solution is introduced to improve the performance of modeling. Finally, a series of experiments for different fault modes are presented to verify the proposed solution.