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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1972

Design and fabrication of prototype system for early warning of impending bearing failure

Ball bearing performance tests run on several identical ball bearings under a variety of load, speed, temperature, and lubrication conditions are reported. Bearing temperature, torque, vibration, noise, strain, cage speed, etc., were monitored to establish those measurements most suitable as indicators of ball bearing health. Tape records were made under steady-state conditions of a variety of speeds and loads. Sample sections were selected for narrowband spectral analysis with a real time analyzer. An artificial flow was created across the inner race surface of one bearing using an acid etch technique to produce the scratch. Tape records obtained before and after established a characteristic frequency response that identifies the presence of the flow. The signals found most useful as indicators of performance degradation were ultrasonic outputs.

Broderick, J. J.↗
DOE OSTI2020

Prognosis of Wind Turbine Gearbox Bearing Failures Using SCADA and Modeled Data

Predictive maintenance and condition monitoring systems for wind turbines have seen increased adoption to minimize downtime, reducing operation and maintenance costs. On today’s wind power plants, the integrated supervisory control and data acquisition (SCADA) system provides low- frequency operational data that can be leveraged to quantify a wind turbine’s health. The aim of this study is to utilize machine-learning techniques to predict axial cracking failures in wind turbine gearbox bearings up to 1 month ahead of time. The failures are assumed to have occurred when the investigated bearing was replaced. While current SCADA systems show the overall condition of a wind turbine, often they do not allow for the investigation of specific gearbox bearings’ health. To enrich bearing fault signatures, additional data are computed through physics-based models using gearbox design information. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score. Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision and F1 score is about 33% and 12% respectively, based on the best modeling case in this study.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
NASA NTRSDate not supplied

Tribology Induced Water Pump Bearing Failure

NASA’s In-Space Propulsion facility experienced two infant mortality water pump bearing failures during a recent refurbishment campaign, the cause or causes of which were unclear. A forensic failure study was undertaken that included site visits, collection and examination of physical evidence and a bearing and pump design review. The pumps are large (2000 hp) vertical turbine design machines that utilize metallic sleeve bearings cooled and lubricated by the pumped fluid (water). Though initial possible root cause for the failures were attributed to improper fabrication and debris contamination, detailed examination of failure surfaces combined with bearing design revealed that the failure mode was thermal seizure brought on by inadequate cooling flow. An unusual contributing factor was that the use of grease as an assembly lubricant, as opposed to a fluid like oil, appears to have exacerbated the rather than alleviated the thermal runaway by blocking water flow. Resolution of the problem was achieved through the design and installation of dedicated bearing lubrication water feed system.

bearings↗
NASA NTRS1972

A system for early warning of bearing failure

System for detecting incipient failure in ball bearings is described. Ultrasonic equipment detects bearing system resonance and provides warning signal through electronic circuitry. Detector can be used to evaluate performance of newly installed bearings. Schematic diagram is provided to show components.

Broderick, J. J.↗
NASA NTRS1992

Investigation of static and cyclic bearing failure mechanisms for GR/EP laminates

Static, cyclic load (fatigue), and residual strength testing of graphite-epoxy (GR/EP) and aluminum pin bearing joints was completed to study bearing failure mechanisms. Parameters investigated included static strength, failure mode, fatigue life, hole growth, joint stiffness, and residual strength. Comparative evaluation of these results show that the MIL-HDBK-5 convention for the definition of bearing strength can be used for GR/EP materials while maintaining the same, or improved, level of structural integrity shown for metal joints.

Walter, R. W.↗
DOE OSTI2021

WhiteWind: White Etching Crack (WEC) Bearing Failures in Wind Turbine: Cooperative Research and Development, CRADA Number CRD-18-00758 (Final Report)

A WEC is a particularly aggressive, unpredictable and wide spread rolling element bearing failure mode that is common for large multi-megawatt (MW) wind turbines. WEC is considered the single most expensive failure mode for all wind turbine components, and there is currently no commercial solution. The Technical University of Denmark (DTU) is leading the WhiteWind project to investigate WECs through funding provided by the Innovation Fund Denmark. Other project partners include Vestas, SKF, Expanite, Rheinisch-Westfalische Technische Hochschule Aachen University (RWTH Aachen), and Argonne National Laboratory. The objective of the overall project is to develop a new surface engineered WEC-resistant bearing material using novel surface engineering techniques that shall provide a commercially competitive alternative to existing wind turbine bearings. NREL will support the project by providing existing measured bearing loads and validating models of drivetrain loads.

17 WIND ENERGY↗
NASA NTRS1974

Design and fabrication of prototype system for early warning of impending bearing failure

A test program was conducted with the objective of developing a method and equipment for on-line monitoring of installed ball bearings to detect deterioration or impending failure of the bearings. The program was directed at the spin-axis bearings of a control moment gyro. The bearings were tested at speeds of 6000 and 8000 rpm, thrust loads from 50 to 1000 pounds, with a wide range of lubrication conditions, with and without a simulated fatigue spall implanted in the inner race ball track. It was concluded that a bearing monitor system based on detection and analysis of modulations of a fault indicating bearing resonance frequency can provide a low threshold of sensitivity.

Meacher, J.↗
NASA NTRS1975

A study of the use of vibration and stress wave sensing for the detection of bearing failure

Results from an experimental study of vibrations and stress waves emitted from ball bearings are presented. Fatique tests were run with both high quality bearings and man faulted bearings, all of one size. Tests were instrumented with different sensors to detect the noises from 10 Hz to 1 MHz. Frequency spectrum plots are presented. The modulation characteristics of the ultrasonic noises were analyzed, and acoustic emission type measurements were conducted. Results are presented which show that there are usable acoustic signal levels even beyond 500 KHz. These signal levels are modulated by a low frequency carrier which is a function of the stress loading and acoustic transmissibility. The results were correlated to fault size in the bearings. The correlation shows that the sensor used for signals from 100 KHz to 1 MHz gave the best sensitivity and detected the generation of very small spalls or pits.

Ensor, L. C.↗
NASA NTRS1993

Detection of bearing failure in mechanical devices using neural networks

We present a novel time-domain method for the detection of faulty bearings that has direct applicability to monitoring the health of the turbo pumps on the Space Shuttle Main Engine. A feed-forward neural network was trained to detect modelled roller bearing faults on the basis of the periodicity of impact pulse trains. The network's performance was dependent upon the number of pulses in the network's input window and the signal-to-noise ratio of the input signal. To test the model's validity, we fit the model's parameters to an actual vibration signal generated by a faulty roller element bearing and applied the network trained on this model to detect faults in actual vibration data. When this network was tested on the actual vibration data, it correctly identified the vibration signal as a fault condition 76 percent of the time.

Burne, Richard A.↗
NASA NTRS1976

Bearing Failure Detection

Shaker Research Corporation, under a contract with NASA, produced a test vehicle that measures resonant frequency of railroad roller bearings to identify source of bearings defects. Marshall scientists have developed a detection concept based on fact that bearing defects themselves excite resonant frequency of rolling elements of bearing as they impact the defect. By detecting resonant frequency and subsequently analyzing character of the signal, bearing defects may be detected and identified as to source. The bearing fault detector will help eliminate this cause of derailments.

Source record↗
NASA NTRS2011

ISS Solar Array Alpha Rotary Joint (SARJ) Bearing Failure and Recovery: Technical and Project Management Lessons Learned

The photovoltaic solar panels on the International Space Station (ISS) track the Sun through continuous rotating motion enabled by large bearings on the main truss called solar array alpha rotary joints (SARJs). In late 2007, shortly after installation, the starboard SARJ had become hard to turn and had to be shut down after exceeding drive current safety limits. The port SARJ, of the same design, had been working well for over 2 years. An exhaustive failure investigation ensued that included multiple extravehicular activities to collect information and samples for engineering forensics, detailed structural and thermal analyses, and a careful review of the build records. The ultimate root cause was determined to be kinematic design vulnerability coupled with inadequate lubrication, and manufacturing flaws; this was corroborated through ground tests, metallurgical studies, and modeling. A highly successful recovery plan was developed and implemented that included replacing worn and damaged components in orbit and applying space-compatible grease to improve lubrication. Beyond the technical aspects, however, lie several key programmatic lessons learned. These lessons, such as running ground tests to intentional failure to experimentally verify failure modes, are reviewed and discussed so they can be applied to future projects to avoid such problems.

DellaCorte, Christopher↗
NASA NTRS1975

ATM CMG bearing failure analysis

The cause or causes for the failure of ATM CMG S/N 5 (Skylab 1) and the anomalies associated with ATM CMG S/N 6 (Skylab 2) were investigated. Skylab telemetry data were reviewed and presented in the form of parameter distributions. The theory that the problems were caused by marginal bearing lubrication was studied along with the effects of orbital conditions on lubricants. Bearing tests were performed to investigate the effect of lubricant or lack of lubricant in the ATM CMG bearings and the dispersion and migration of the lubricant. The vacuum and weightless conditions of space were simulated in the bearing tests. Analysis of the results of the tests conducted points to inadequate lubrication as the predominant factor causing the failure of ATM CMG S/N 5 (Skylab 1) and the anomalies associated with ATM CMG S/N 6 (Skylab 2).

Source record↗
NASA NTRS1976

An evaluation of dry film lubricants and substrate materials for use on SSME gimbal bearings

Failure of the spherical bearing shaft of the Space Shuttle Main Engine (SSME) gimbal bearing assembly was encountered during Design Verification Specification testing of the full scale engine. Investigation revealed that the failure was caused by a deficiency in the lubrication system. Based upon the materials and gimbal operating conditions, a lubricant of MoS2 and graphite with a ceramic binder was the best lubricant candidate for this particular application; however, the decision to implement the change was not made without verification testing. Scaled down simulation testing was performed. Four different substrate materials and eight different dry film lubricants were subjected to tests under simulated SSME environmental and stress load conditions. The test specimens were evaluated for friction and operating life. Each test specimen was subjected to cyclic operation under load until failure. The force required to move the bearing surfaces relative to each other was monitored throughout the test, thus providing analytical data for derivation of the coefficient of friction. Results indicate that the MoS2/graphite lubricant with ceramic binder proved to be superior from the standpoint of endurance and also from the standpoint of friction reducing capabilities when applied to the titanium substrate material used on SSME. Endurance of this lubricant was approximately 16 times that of the lubricant which was being used when the SSME gimbal failed.

Harp, J. A.↗
NASA NTRS1982

Incipient failure detection (IFD) of SSME ball bearings

Because of the immense noise background during the operation of a large engine such as the SSME, the relatively low level unique ball bearing signatures were often buried by the overall machine signal. As a result, the most commonly used bearing failure detection technique, pattern recognition using power spectral density (PSD) constructed from the extracted bearing signals, is rendered useless. Data enhancement techniques were carried out by using a HP5451C Fourier Analyzer. The signal was preprocessed by a Digital Audio Crop. DAC-1024I noise cancelling filter in order to estimate the desired signal corrupted by the backgound noise. Reference levels of good bearings were established. Any deviation of bearing signals from these reference levels indicate the incipient bearing failures.

Source record↗
DOE OSTI2021

Machine Learning for Gearbox Fault Prediction by Using Both Scada and Modeled Data

This presentation outlines the work in the paper titled "Prognostics of Wind Turbine Gearbox Bearing Failures Using SCADA and Modeled Data" published by the PHM Society and presented at its 2020 annual conference. It is accessible at https://papers.phmsociety.org/index.php/phmconf/article/download/1292/862. The technical work is on machine learning approaches for prognostics for gearbox faults. The methodology combines SCADA time series data and physics domain modeling data, derived from the models developed by the NREL team, as inputs to machine learning models to predict gearbox bearing failures with one month lead time. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score, and AUC (area under receiver operating characteristic curve). Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision, and F1 score, and AUC is about 33%, and 12%, and 6% respectively, based on the best modeling case in this study.

49 EE - Wind and Water Power Program - Wind (EE-4W↗