Mechanical Engineering

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    Application of Diagnostics and Prognostics Techniques to Qualification Against Wear-Out Failure
    (2022) Ram, Abhishek; Das, Diganta; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    QUALIFICATION IS A PROCESS THAT DEMONSTRATES WHETHER A PRODUCT MEETS OR EXCEEDS SPECIFIED REQUIREMENTS. TESTING AND DATA ANALYSIS PERFORMED WITHIN A QUALIFICATION PROCEDURE SHOULD VERIFY IF PRODUCTS SATISFY THOSE REQUIREMENTS, INCLUDING RELIABILITY REQUIREMENTS. MOST OF THE ELECTRONICS INDUSTRY QUALIFIES PRODUCTS USING PROCEDURES DICTATED WITHIN QUALIFICATION STANDARDS. A REVIEW OF COMMON QUALIFICATION STANDARDS REVEALS THAT THOSE STANDARDS DO NOT CONSIDER CUSTOMER REQUIREMENTS OR THE PRODUCT PHYSICS-OF-FAILURE IN THAT INTENDED APPLICATION. AS A RESULT, QUALIFICATION, AS REPRESENTED IN THE REVIEWED QUALIFICATION STANDARDS, WOULD NOT MEET OUR DEFINITION OF QUALIFICATION FOR RELIABILITY ASSESSMENT. THIS THESIS PROVIDES AN APPLICATION-SPECIFIC APPROACH FOR DEVELOPING A QUALIFICATION PROCEDURE THAT ACCOUNTS FOR CUSTOMER REQUIREMENTS, PRODUCT PHYSICS-OF-FAILURE, AND KNOWLEDGE OF PRODUCT BEHAVIOR UNDER LOADING. THIS THESIS PROVIDES A REVAMPED APPROACH FOR DEVELOPING A LIFE CYCLE PROFILE THAT ACCOUNTS FOR LOADING THROUGHOUT MANUFACTURING/ASSEMBLY, STORAGE AND TRANSPORTATION, AND OPERATION. THE THESIS ALSO DISCUSSES IDENTIFYING VARIATIONS IN THE LIFE CYCLE PROFILE THAT MAY ARISE THROUGHOUT THE PRODUCT LIFETIME AND METHODS FOR ESTIMATING LOADS. THIS UPDATED APPROACH FOR DEVELOPING A LIFE CYCLE PROFILE SUPPORTS BETTER FAILURE PRIORITIZATION, TEST SELECTION, AND TEST CONDITION AND DURATION REQUIREMENT ESTIMATION. ADDITIONALLY, THIS THESIS INTRODUCES THE APPLICATION OF DIAGNOSTICS AND PROGNOSTICS TECHNIQUES TO ANALYZE REAL-TIME DATA TRENDS WHILE CONDUCTING QUALIFICATION TESTS. DIAGNOSTICS TECHNIQUES IDENTIFY ANOMALOUS BEHAVIOR EXHIBITED BY THE PRODUCT, AND PROGNOSTICS TECHNIQUES FORECAST HOW THE PRODUCT WILL BEHAVE DURING THE REMAINDER OF THE QUALIFICATION TEST AND HOW THE PRODUCT WOULD HAVE BEHAVED IF THE TEST CONTINUED. AS A RESULT, COMBINING DIAGNOSTICS AND PROGNOSTICS TECHNIQUES CAN ENABLE THE PREDICTION OF THE REMAINING TIME-TO-FAILURE FOR THE PRODUCT UNDERGOING QUALIFICATION. SEVERAL ANCILLARY BENEFITS RELATED TO AN IMPROVED TESTING STRATEGY, PARTS SELECTION AND MANAGEMENT, AND SUPPORT OF A PROGNOSTICS AND HEALTH MANAGEMENT SYSTEM IN OPERATION ALSO ARISE FROM APPLYING PROGNOSTICS AND DIAGNOSTICS TECHNIQUES TO QUALIFICATION.
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    DEEP ADVERSARIAL APPROACHES IN RELIABILITY
    (2020) Verstraete, David Benjamin; Modarres, Mohammad; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Reliability engineering has long been proposed with the problem of predicting failures using all available data. As modeling techniques have become more sophisticated, so too have the data sources from which reliability engineers can draw conclusions. The Internet of Things (IoT) and cheap sensing technologies have ushered in a new expansive set of multi-dimensional big machinery data in which previous reliability engineering modeling techniques remain ill-equipped to handle. Therefore, the objective of this dissertation is to develop and advance reliability engineering research by proposing four comprehensive deep learning methodologies to handle these big machinery data sets. In this dissertation, a supervised fault diagnostic deep learning approach with applications to the rolling element bearings incorporating a deep convolutional neural network on time-frequency images was developed. A semi-supervised generative adversarial networks-based approach to fault diagnostics using the same time-frequency images was proposed. The time-frequency images were used again in the development of an unsupervised generative adversarial network-based methodology for fault diagnostics. Finally, to advance the studies of remaining useful life prediction, a mathematical formulation and subsequent methodology to combine variational autoencoders and generative adversarial networks within a state-space modeling framework to achieve both unsupervised and semi-supervised remaining useful life estimation was proposed. All four proposed contributions showed state of the art results for both fault diagnostics and remaining useful life estimation. While this research utilized publicly available rolling element bearings and turbofan engine data sets, this research is intended to be a comprehensive approach such that it can be applied to a data set of the engineer’s chosen field. This research highlights the potential for deep learning-based approaches within reliability engineering problems.
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    PROGNOSTICS-BASED QUALIFICATION OF WHITE LIGHT-EMITTING DIODES (LEDS)
    (2014) Chang, Moon-Hwan; Pecht, Michael G.; Ayyub, Bilal; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Light-emitting diode (LED) applications have expanded from display backlighting in computers and smart phones to more demanding applications including automotive headlights and street lightening. With these new applications, LED manufacturers must ensure that their products meet the performance requirements expected by end users, which in many cases require lifetimes of 10 years or more. The qualification tests traditionally conducted to assess such lifetimes are often as long as 6,000 hours, yet even this length of time does not guarantee that the lifetime requirements will be met. This research aims to reduce the qualification time by employing anomaly detection and prognostic methods utilizing optical, electrical, and thermal parameters of LEDs. The outcome of this research will be an in-situ monitoring approach that enables parameter sensing, data acquisition, and signal processing to identify the potential failure modes such as electrical, thermal, and optical degradation during the qualification test. To detect anomalies, a similarity-based-metric test has been developed to identify anomalies without utilizing historical libraries of healthy and unhealthy data. This similarity-based-metric test extracts features from the spectral power distributions using peak analysis, reduces the dimensionality of the features by using principal component analysis, and partitions the data set of principal components into groups using a 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 analysis, dominant degradation processes associated with the LED die and phosphors in the LED package can be identified. When implemented, the results of this research will enable a short qualification time. Prognostics of LEDs are developed with spectral power distribution (SPD) prediction for color failure. SPD is deconvoluted with die SPD and phosphor SPD with asymmetric double sigmoidal functions. Future SPD is predicted by using the particle filter algorithm to estimate the propagating parameters of the asymmetric double sigmoidal functions. Diagnostics is enabled by SPD prediction to indicate die degradation, phosphor degradation, or package degradation based on the nature of degradation shape of SPD. SPDs are converted to light output and 1976 CIE color coordinates using colorimetric conversion with color matching functions. Remaining useful life (RUL) is predicted using 7-step SDCM (standard deviation of color matching) threshold (i.e., 0.007 color distance in the CIE 1676 chromaticity coordinates). To conduct prognostics utilizing historical libraries of healthy and unhealthy data from other devices, this research employs similarity-based statistical measures for a prognostics-based qualification method using optical, electrical, and thermal covariates as health indices. Prognostics is conducted using the similarity-based statistical measure with relevance vector machine regression to capture degradation trends. Historical training data is used to extract features and define failure thresholds. Based on the relevance vector machine regression results, which construct the background health knowledge from historical training units, the similarity weight is used to measure the similarity between each training unit and test unit under the test. The weighted sum is then used to estimate the remaining useful life of the test unit.
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    DEVELOPMENT OF DIAGNOSTIC AND PROGNOSTIC METHODOLOGIES FOR ELECTRONIC SYSTEMS BASED ON MAHALANOBIS DISTANCE
    (2009) Kumar, Sachin; Pecht, Michael; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Diagnostic and prognostic capabilities are one aspect of the many interrelated and complementary functions in the field of Prognostic and Health Management (PHM). These capabilities are sought after by industries in order to provide maximum operational availability of their products, maximum usage life, minimum periodic maintenance inspections, lower inventory cost, accurate tracking of part life, and no false alarms. Several challenges associated with the development and implementation of these capabilities are the consideration of a system's dynamic behavior under various operating environments; complex system architecture where the components that form the overall system have complex interactions with each other with feed-forward and feedback loops of instructions; the unavailability of failure precursors; unseen events; and the absence of unique mathematical techniques that can address fault and failure events in various multivariate systems. The Mahalanobis distance methodology distinguishes multivariable data groups in a multivariate system by a univariate distance measure calculated from the normalized value of performance parameters and their correlation coefficients. The Mahalanobis distance measure does not suffer from the scaling effect--a situation where the variability of one parameter masks the variability of another parameter, which happens when the measurement ranges or scales of two parameters are different. A literature review showed that the Mahalanobis distance has been used for classification purposes. In this thesis, the Mahalanobis distance measure is utilized for fault detection, fault isolation, degradation identification, and prognostics. For fault detection, a probabilistic approach is developed to establish threshold Mahalanobis distance, such that presence of a fault in a product can be identified and the product can be classified as healthy or unhealthy. A technique is presented to construct a control chart for Mahalanobis distance for detecting trends and biasness in system health or performance. An error function is defined to establish fault-specific threshold Mahalanobis distance. A fault isolation approach is developed to isolate faults by identifying parameters that are associated with that fault. This approach utilizes the design-of-experiment concept for calculating residual Mahalanobis distance for each parameter (i.e., the contribution of each parameter to a system's health determination). An expected contribution range for each parameter estimated from the distribution of residual Mahalanobis distance is used to isolate the parameters that are responsible for a system's anomalous behavior. A methodology to detect degradation in a system's health using a health indicator is developed. The health indicator is defined as the weighted sum of a histogram bin's fractional contribution. The histogram's optimal bin width is determined from the number of data points in a moving window. This moving window approach is utilized for progressive estimation of the health indicator over time. The health indicator is compared with a threshold value defined from the system's healthy data to indicate the system's health or performance degradation. A symbolic time series-based health assessment approach is developed. Prognostic measures are defined for detecting anomalies in a product and predicting a product's time and probability of approaching a faulty condition. These measures are computed from a hidden Markov model developed from the symbolic representation of product dynamics. The symbolic representation of a product's dynamics is obtained by representing a Mahalanobis distance time series in symbolic form. Case studies were performed to demonstrate the capability of the proposed methodology for real time health monitoring. Notebook computers were exposed to a set of environmental conditions representative of the extremes of their life cycle profiles. The performance parameters were monitored in situ during the experiments, and the resulting data were used as a training dataset. The dataset was also used to identify specific parameter behavior, estimate correlation among parameters, and extract features for defining a healthy baseline. Field-returned computer data and data corresponding to artificially injected faults in computers were used as test data.