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DOE OSTI2021

Remote Monitoring and Diagnostics of Pitch-Bearing Defects in an MW-Scale Wind Turbine Using Pitch Symmetrical-Component Analysis

Recently, multiple wind turbine failure databases have reviewed that the pitch system is one of the subassemblies with the highest failure rates and largest contributors to the overall downtime. Therefore, there has been an increasing interest to provide remote health monitoring for wind turbine pitch system. While most of the research articles are discussing pitch actuation system (hydraulic or electric actuator) faults only, there is very limited research on pitch-bearing-defect detection. This article provides a remote and hardware-free solution to monitor multiaxis pitch-bearing health condition called pitch symmetrical-component analysis. It leverages readily available low-resolution (100 Hz) electrical measurements, mechanical measurements, and control signals from the existing pitch control platform, and innovatively applies symmetrical-component analysis in multiphase ac system to multiaxis pitch control system and introduces multiaxis pitch-bearing degradation trending curves. This hardware-free solution can be directly applied to the existing wind turbines and successfully give the wind farm operator an early warning before multiaxis pitch bearing fails. It has been proved to be accurate, low cost, and has minimum impacts on turbine normal operation, and has been validated by field data from several North America MW-scale wind farms. This approach turns out to be the first hardware-free (no additional hardware needed) method to remotely monitor and diagnose multiaxis wind turbine pitch-bearing condition.

17 WIND ENERGY↗

DOE OSTI2022

Investigation of Multiple Data Streams for Gearbox Bearing Fault Prediction Through Machine-Learning Models

Operations and maintenance (O&M) cost of wind plant accounts up to 30% of total energy cost, which can be reduced through continuous monitoring and successfully detecting incipient wind turbine failures. To accomplish this, condition monitoring and predictive maintenance systems are being implemented in wind industry to support O&M decision making. A wide range of approaches for condition monitoring and fault prediction have been developed. These approaches generally use historical data of wind turbines collected by Supervisory Control and Data Acquisition (SCADA) system to identify patterns that lead to failure. These SCADA data show the overall condition of a wind turbine and can be leveraged to detect when the turbine's performance is degrading and to identify if a fault is developing. However, it becomes challenging to predict the failure of a specific wind turbine gearbox bearing, because the SCADA data are often not directly linked to the component. To bridge the gap, we have investigated features calculated from SCADA data using physics-based models and the gearbox design over the years. The damaged metric we used in the physics domain is frictional energy. Combining these physics domain variables with SCADA data as inputs to various machine learning models for gearbox bearing fault prediction, we have demonstrated the benefits of leveraging both physics and data domain models. It was an attempt to improve frictional-energy-based damage metric by adding data domain inputs, as we had learned that the frictional-energy-based damage metric alone is not sufficient to single out failed bearings from healthy. As condition monitoring data (either vibration or oil debris data) has become available at more and more wind plants, we would like to evaluate whether by adding the condition monitoring data can help further improve the performance of frictional-energy-based damage metric for gearbox bearing fault prediction. Both cases by modeling through various machine learning algorithms are discussed in this study along with some observations.

fault prediction↗

NASA NTRS1997

Health Monitoring of Composite Material Structures using a Vibrometry Technique

Large composite material structures such as aircraft and Reusable Launch Vehicles (RLVS) operate in severe environments comprised of vehicle dynamic loads, aerodynamic loads, engine vibration, foreign object impact, lightning strikes, corrosion, and moisture absorption. These structures are susceptible to damage such as delamination, fiber breaking/pullout, matrix cracking, and hygrothermal strain. To ensure human safety and load-bearing integrity, these structures must be inspected to detect and locate often invisible damage and faults before becoming catastrophic. Moreover, nearly all future structures will need some type of in-service inspection technique to increase their useful life and reduce maintenance and overall costs. Possible techniques for monitoring the health and indicating damage on composite structures include: c-scan, thermography, acoustic emissions using piezoceramic actuators or fiber-optic wires with gratings, laser ultrasound, shearography, holography, x-ray, and others. These techniques have limitations in detecting damage that is beneath the surface of the structure, far away from a sensor location, or during operation of the vehicle. The objective of this project is to develop a more global method for damage detection that is based on structural dynamics principles, and can inspect for damage when the structure is subjected to vibratory loads to expose faults that may not be evident by static inspection. A Transmittance Function Monitoring (TFM) method is being developed in this project for ground-based inspection and operational health monitoring of large composite structures as a RLV. A comparison of the features of existing health monitoring approaches and the proposed TFM method is given.

Schulz, Mark J.↗

NASA NTRS1998

A New Approach to Overcoming Spatial Aliasing in Structural Damage Detection

Aircraft, reusable launch vehicles,unmanned aircraft, and other advanced structures are being built using lightweight composite materials/metals with design safety factors as low as 1.25. These advanced structures operate in uncertain and severe environments and are susceptible to damage such as delamination, fiber/matrix damage, hydrothermal strain in composite materials, and fatigue and cracking in metals. To ensure human safety and load-bearing integrity these structures must be inspected to detect and locate often invisible damage and faults before they become catastrophic. Conventional methods of non-destructive evaluation sometimes miss significant damage and are time consuming and expensive to perform. In contrast, vibrometry or vibration signature techniques are a global method of structural integrity monitoring that potentially can efficiently detect damage on large structures, including damage that is away from sensor locations, and in the interior of structures. However, a barrier problem in damage detection using vibration measurements is the need to measure the vibration response at a large number of points on the structure. Typically, model reduction or expansion procedures such as Guyan reduction or dynamic expansion are attempted to overcome the problem of insufficient measurements or spatial aliasing. These approaches depend on using information from the healthy model, and thus put error into the reduction used to represent the damaged structure. In this paper, a Frequency Response Function technique is used to detect damage to a fixed-free beam. The technique uses measured frequency response functions from the healthy structure as reference data, and then monitors vibration measurements during the life of the structure to detect damage. In an analytical simulation using a finite-element model of a beam, damage was located using only sparse measurements because the technique uses both rotation and translation measurements from the damaged structure.

Schulz, Mark J.↗

NASA NTRS1996

Feasibility Study of a Rotorcraft Health and Usage Monitoring System (HUMS): Results of Operator's Evaluation

The objective was to evaluate the feasibility of a state-of-the-art health and usage monitoring system (HUMS) to provide monitoring of critical mechanical systems on the helicopter, including motors, drive train, engines, and life-limited components. The implementation of HUMS and cost integration with current maintenance procedures was assessed from the operator's viewpoint in order to achieve expected benefits from these systems, such as enhanced safety, reduced maintenance cost, and increased availability. An operational HUMS that was installed and operated under an independent flight trial program was used as a basis for this study. The HUMS equipment and software were commercially available. Based on the results of the feasibility study, the HUMS used in the flight trial program generally demonstrated a high level of reliability in monitoring the rotor system, engines, drive train, and life-limited components. The system acted as a sentinel to warn of impending failures. A worn tail rotor pitch bearing was detected by HUMS, which had the capability for self testing to diagnose system and sensor faults. Examples of potential payback to the operator with HUMS were identified, including reduced insurance cost through enhanced safety, lower operating costs derived from maintenance credits, increased aircraft availability, and improved operating efficiency. The interfacing of HUMS with current operational procedures was assessed to require only minimal revisions to the operator's maintenance manuals. Finally the success in realizing the potential benefits from HUMS technology was found to depend on the operator, helicopter manufacturer, regulator (FAA), and HUMS supplier working together.

Romero, Raylund↗

NASA NTRS2013

Vibration Based Sun Gear Damage Detection

Seeded fault experiments were conducted on the planetary stage of an OH-58C helicopter transmission. Two vibration based methods are discussed that isolate the dynamics of the sun gear from that of the planet gears, bearings, input spiral bevel stage, and other components in and around the gearbox. Three damaged sun gears: two spalled and one cracked, serve as the focus of this current work. A non-sequential vibration separation algorithm was developed and the resulting signals analyzed. The second method uses only the time synchronously averaged data but takes advantage of the signal/source mapping required for vibration separation. Both algorithms were successful in identifying the spall damage. Sun gear damage was confirmed by the presence of sun mesh groups. The sun tooth crack condition was inconclusive.

diagnostics↗

DOE OSTI2025

Federated Learning for Efficient Condition Monitoring and Anomaly Detection in Industrial Cyber-Physical Systems

Detecting and localizing anomalies in cyber-physical systems (CPS) has become increasingly challenging as systems grow in complexity, particularly due to varying sensor reliability and node failures in distributed environments. While federated learning (FL) offers a foundation for distributed model training, existing approaches lack mechanisms to handle these CPS-specific challenges. This paper presents an enhanced FL framework that introduces three key innovations: adaptive model aggregation based on sensor reliability, dynamic node selection for resource optimization, and Weibull-based checkpointing for fault tolerance. Our framework enables reliable condition monitoring while addressing the computational and reliability challenges of industrial CPS deployments. Experiments on NASA Bearing and Hydraulic System Datasets demonstrate superior performance over state-of-the-art FL methods, achieving 99.5% AUC-ROC in anomaly detection and maintaining accuracy under node failures. Statistical validation using Mann-Whitney (U) test confirms significant improvements (p < 0.05) in both detection accuracy and computational efficiency across diverse operational scenarios.1

Marfo, William [University of Texas at El Paso,Dep↗

NASA NTRS2009

Functional Fault Modeling of a Cryogenic System for Real-Time Fault Detection and Isolation

When setting out to model and/or simulate a complex mechanical or electrical system, a modeler is faced with a vast array of tools, software, equations, algorithms and techniques that may individually or in concert aid in the development of the model. Mature requirements and a well understood purpose for the model may considerably shrink the field of possible tools and algorithms that will suit the modeling solution. Is the model intended to be used in an offline fashion or in real-time? On what platform does it need to execute? How long will the model be allowed to run before it outputs the desired parameters? What resolution is desired? Do the parameters need to be qualitative or quantitative? Is it more important to capture the physics or the function of the system in the model? Does the model need to produce simulated data? All these questions and more will drive the selection of the appropriate tools and algorithms, but the modeler must be diligent to bear in mind the final application throughout the modeling process to ensure the model meets its requirements without needless iterations of the design. The purpose of this paper is to describe the considerations and techniques used in the process of creating a functional fault model of a liquid hydrogen (LH2) system that will be used in a real-time environment to automatically detect and isolate failures.

Ferrell, Bob↗

NASA NTRS2005

Intelligent Engine Systems Work Element 1.3: Sub System Health Management

The objectives of this program were to develop health monitoring systems and physics-based fault detection models for engine sub-systems including the start, lubrication, and fuel. These models will ultimately be used to provide more effective sub-system fault identification and isolation to reduce engine maintenance costs and engine down-time. Additionally, the bearing sub-system health is addressed in this program through identification of sensing requirements, a review of available technologies and a demonstration of a demonstration of a conceptual monitoring system for a differential roller bearing. This report is divided into four sections; one for each of the subtasks. The start system subtask is documented in section 2.0, the oil system is covered in section 3.0, bearing in section 4.0, and the fuel system is presented in section 5.0.

Ashby, Malcolm↗

NASA NTRS1992

AVIRIS as a tool for carbonatite exploration: Comparison of SPAM and Mbandmap data analysis methods

Data acquired with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) of the Mountain Pass, San Bernadino County, California, area were analyzed to evaluate the use of narrow-band imaging data for carbonatite exploration. Carbonatites are igneous carbonate-rich rocks that are economically important in part because they are the major source for rare-earth minerals. Because the 224 AVIRIS spectral channels have a nominal spectral resolution of 10 nm, narrow absorption features such as those displayed by the rare-earth elements neodymium (Nd) and samarium (Sm) may be detected. The Mountain Pass region encompasses a well-exposed sequence of sedimentary, metamorphic, and igneous rocks, including an alkalic carbonatite intrusion. The carbonatite was emplaced in Precambrian granitic rocks that are fault bounded by a thick suite of Paleozoic, sedimentary rock, predominantly dolomite. The carbonatite stock, the major source of light rare-earth elements (REE) in the United States, affords the opportunity to test AVIRIS capabilities for detecting REE absorption features. Nd-bearing minerals display narrow, sharp and distinctive spectral features at 580, 740, 800, and 870 nm. Minerals bearing Sm display similarly sharp features near 1090, 1250, 1410, and 1550 nm. The more common REE, lanthanum and cerium, do not display absorption features in the visible/near-infrared region in their natural oxidation states.

Kingston, Marguerite J.↗

NASA NTRS2005

Hybrid Bearing Prognostic Test Rig

The NASA Glenn Research Center has developed a new Hybrid Bearing Prognostic Test Rig to evaluate the performance of sensors and algorithms in predicting failures of rolling element bearings for aeronautics and space applications. The failure progression of both conventional and hybrid (ceramic rolling elements, metal races) bearings can be tested from fault initiation to total failure. The effects of different lubricants on bearing life can also be evaluated. Test conditions monitored and recorded during the test include load, oil temperature, vibration, and oil debris. New diagnostic research instrumentation will also be evaluated for hybrid bearing damage detection. This paper summarizes the capabilities of this new test rig.

Dempsey, Paula J.↗

NASA NTRS1972

To evaluate ERTS-1 data for usefulness as a geological sensor in the diverse geological terranes of New York State

The author has identified the following significant results. In the present imagery, obtained during the full foliage of summer and fall, the greatest amount of spectral geology is displayed in the Adirondack region where bedrock geology is strongly linked to topography. Of the four spectral bands imaged, band 5 and 7 provide the most geological information. The boundary between the basement rocks of the Adirondack Dome and the surrounding Lower Paleozoic rocks is well delineated except in the Northwest Lowlands and along parts of the eastern Adirondacks. Within the basement complex, the most prominently displayed features are numerous north-northeast trending faults and topographic lineaments, and arcuate east-west valleys developed in some of the weaker metasedimentary rocks. The majority of the faults and lineaments shown on the geologic map of New York appear in the ERTS-1 imagery. In addition, many new linears were detected, as well as a number of anomalous curvilinear elements, some circular in plan and measuring up to 25 km in diameter, which do not bear any clear relationship to mapped geological contacts. The possibility that it is an astrobleme will be investigated after snow melts in the spring.

Isachsen, Y. W.↗

NASA NTRS1997

Diagnostic Analyzer for Gearboxes (DAG): User's Guide

This documentation describes the Diagnostic Analyzer for Gearboxes (DAG) software for performing fault diagnosis of gearboxes. First, the user would construct a graphical representation of the gearbox using the gear, bearing, shaft, and sensor tools contained in the DAG software. Next, a set of vibration features obtained by processing the vibration signals recorded from the gearbox using a signal analyzer is required. Given this information, the DAG software uses an unsupervised neural network referred to as the Fault Detection Network (FDN) to identify the occurrence of faults, and a pattern classifier called Single Category-Based Classifier (SCBC) for abnormality scaling of individual vibration features. The abnormality-scaled vibration features are then used as inputs to a Structure-Based Connectionist Network (SBCN) for identifying faults in gearbox subsystems and components. The weights of the SBCN represent its diagnostic knowledge and are derived from the structure of the gearbox graphically presented in DAG. The outputs of SBCN are fault possibility values between 0 and 1 for individual subsystems and components in the gearbox with a 1 representing a definite fault and a 0 representing normality. This manual describes the steps involved in creating the diagnostic gearbox model, along with the options and analysis tools of the DAG software.

Jammu, Vinay B.↗

NASA NTRS1997

Fault Detection of Rotating Machinery using the Spectral Distribution Function

The spectral distribution function is introduced to characterize the process leading to faults in rotating machinery. It is shown to be a more robust indicator than conventional power spectral density estimates, but requires only slightly more computational effort. The method is illustrated with examples from seeded gearbox transmission faults and an analytical model of a defective bearing. Procedures are suggested for implementation in realistic environments.

Davis, Sanford S.↗

NASA NTRS1994

Analytical and Experimental Vibration Analysis of a Faulty Gear System

A comprehensive analytical procedure was developed for predicting faults in gear transmission systems under normal operating conditions. A gear tooth fault model is developed to simulate the effects of pitting and wear on the vibration signal under normal operating conditions. The model uses changes in the gear mesh stiffness to simulate the effects of gear tooth faults. The overall dynamics of the gear transmission system is evaluated by coupling the dynamics of each individual gear-rotor system through gear mesh forces generated between each gear-rotor system and the bearing forces generated between the rotor and the gearbox structure. The predicted results were compared with experimental results obtained from a spiral bevel gear fatigue test rig at NASA Lewis Research Center. The Wigner-Ville distribution (WVD) was used to give a comprehensive comparison of the predicted and experimental results. The WVD method applied to the experimental results were also compared to other fault detection techniques to verify the WVD's ability to detect the pitting damage, and to determine its relative performance. Overall results show good correlation between the experimental vibration data of the damaged test gear and the predicted vibration from the model with simulated gear tooth pitting damage. Results also verified that the WVD method can successfully detect and locate gear tooth wear and pitting damage.

Choy, F. K.↗

NASA NTRS2008

Signal Detection Theory Applied to Helicopter Transmission Diagnostic Thresholds

Helicopter Health Usage Monitoring Systems (HUMS) have potential for providing data to support increasing the service life of a dynamic mechanical component in the transmission of a helicopter. Data collected can demonstrate the HUMS condition indicator responds to a specific component fault with appropriate alert limits and minimal false alarms. Defining thresholds for specific faults requires a tradeoff between the sensitivity of the condition indicator (CI) limit to indicate damage and the number of false alarms. A method using Receiver Operating Characteristic (ROC) curves to assess CI performance was demonstrated using CI data collected from accelerometers installed on several UH60 Black Hawk and AH64 Apache helicopters and an AH64 helicopter component test stand. Results of the analysis indicate ROC curves can be used to reliably assess the performance of commercial HUMS condition indicators to detect damaged gears and bearings in a helicopter transmission.

Dempsey, Paula J.↗

NASA NTRS2009

Signal Detection Theory Applied to Helicopter Transmission Diagnostic Thresholds

Dempsey, Paula J.↗

NASA NTRS2002

On-Line, Gyro-Based, Mass-Property Identification for Thruster-Controlled Spacecraft Using Recursive Least Squares

Spacecraft control, state estimation, and fault-detection-and-isolation systems are affected by unknown v aerations in the vehicle mass properties. It is often difficult to accurately measure inertia terms on the ground, and mass properties can change on-orbit as fuel is expended, the configuration changes, or payloads are added or removed. Recursive least squares -based algorithms that use gyro signals to identify the center of mass and inverse inertia matrix are presented. They are applied in simulation to 3 thruster-controlled vehicles: the X-38 and Mini-AERCam under development at NASA-JSC, and the SAM, an air-bearing spacecraft simulator at the NASA-Ames Smart Systems Research Lab (SSRL).

Wilson, Edward↗