Chang M.-H.,University of Maryland University College |
Chang M.-H.,Samsung |
Chen C.,University of Maryland University College |
Chen C.,DEI Group |
And 2 more authors.
IEEE Transactions on Industrial Informatics | Year: 2014
Today's decreasing product development cycle time requires rapid and cost-effective reliability analysis and testing. Qualification is the process of demonstrating that a product is capable of meeting or exceeding specified requirements. Light-emitting diode (LED) qualification tests are often as long as 6000 h, but this length of time does not guarantee the typically required lifetime of 10 years or more. This paper presents a prognostics-based technique that reduces the LED qualification time. An anomaly detection technique called the similarity-based metric test is developed to identify anomalies without utilizing historical libraries of healthy and unhealthy data. The similarity-based metric test extracts features from the spectral power distributions (SPDs) using peak analysis, reduces the dimensionality of the features using principal component analysis, and partitions the data set of principal components into groups using a k-nearest neighbor (KNN)-kernel density-based clustering technique. A detection algorithm then evaluates the distances from the centroid of each cluster to each test point and detects anomalies when the distance is greater than the threshold. From this, the dominant degradation processes associated with the LED die and phosphors in the LED package can be identified. In our case study, anomalies were detected at less than 1200 h using the similarity-based metric test. Thus, our method could decrease the amount of LED qualification testing time by providing users with an earlier time to begin remaining useful life prediction without waiting 6000 h as required by industrial standards. © 2005-2012 IEEE.
Van Hecke B.,University of Illinois at Chicago |
Qu Y.,DEI Group |
He D.,University of Illinois at Chicago |
Bechhoefer E.,Green Power Monitoring Systems LLC
Journal of Failure Analysis and Prevention | Year: 2014
The diagnosis of bearing health through the quantification of accelerometer data has been an area of interest for many years and has resulted in numerous signal processing methods and algorithms. This paper proposes a new diagnostic approach that combines envelope analysis, time synchronous resampling, and spectral averaging of vibration signals to extract condition indicators (CIs) used for rolling-element bearing fault diagnosis. First, the accelerometer signal is digitized simultaneously with tachometer signal acquisition. Then, the digitized vibration signal is band pass filtered to retain the information associated with the bearing defects. Finally, the tachometer signal is used to time synchronously resample the vibration data which allows the computation of a spectral average and the extraction of the CIs used for bearing fault diagnosis. The proposed technique is validated using the vibration output of seeded fault steel bearings on a bearing test rig. The result is an effective approach validated to diagnose all four bearing fault types: inner race, outer race, ball, and cage. © 2014 ASM International.
Dundics M.,DEI Group |
Finley B.,U.S. Navy |
Krooner K.,Esrg Llc |
Roche T.,U.S. Navy |
Rodgers R.,U.S. Navy
Proceedings of the ASME Turbo Expo | Year: 2010
The development of the Littoral Combat Ship (LCS) and its life cycle support design objectives were driven by three key objectives: 1) High level of ship mission availability while performing any one of the three mission capabilities; 2) Minimal Total Ownership Cost (TOC); 3) Manning compliment lower than the similar predecessor class of ships. To achieve these concurrent goals, the ship design provides functionality including advanced automation for machinery control, as well as mission function reconfiguration and execution. Unfortunately, information-based automated machinery reliability management decision support was not part of the ship design. This type of decision support is vital in enabling a significantly reduced crew and the advance planning required for executing the scheduled short maintenance availabilities. Leveraging existing equipment monitoring technologies deployed throughout the legacy fleet with the reliability engineering approach on LCS will improve the operational availability of gas turbine propulsion systems and allow executing the ship's Concept of Operations (CONOPS). To address the reliability and TOC risks with the initially defined sustainment approach, a Reliability Engineering derived Condition Based Maintenance (CBM) strategy was developed, such that it could be implemented using a proven remote monitoring infrastructure. This paper will describe the Reliability Engineering based CBM approach and how it will be implemented on the LCS-1 and LCS-2 propulsion gas turbine engines and other critical systems to achieve system level operational reliability, the LCS life cycle support design objectives, and defined sustainment strategies. Copyright © 2010 by ASME.
Shoukry S.N.,West Virginia University |
Luo Y.,DEI Group |
Riad M.Y.,West Virginia University |
William G.W.,West Virginia University
Smart Structures and Systems | Year: 2013
In this paper, a wireless sensing system for structural field evaluation and rating of bridges is presented. The system uses a wireless platform integrated with traditional analogue sensors including strain gages and accelerometers along with the operating software. A wireless vehicle position indicator is developed using a tri-axial accelerometer node that is mounted on the test vehicle, and was used for identifying the moving truck position during load testing. The developed software is capable of calculating the theoretical bridge rating factors based on AASHTO Load and Resistance Factor Rating specifications, and automatically produces the field adjustment factor through load testing data. The sensing system along with its application in bridge deck rating was successfully demonstrated on the Evansville Bridge in West Virginia. A finite element model was conducted for the test bridge, and was used to calculate the load distribution factors of the bridge deck after verifying its results using field data. A confirmation field test was conducted on the same bridge and its results varied by only 3% from the first test. The proposed wireless sensing system proved to be a reliable tool that overcomes multiple drawbacks of conventional wired sensing platforms designed for structural load evaluation of bridges. Copyright © 2013 Techno-Press, Ltd.