The method for importance analysis of failure mode based on EDA simulation

Uncertainty plays a role in nearly all aspects of prognostic health management (PHM) systems. Aleatory uncertainty from inherently-variable inputs such as material properties, epistemic uncertainty from a lack of knowledge about the system and its inputs, and ontological uncertainty due to completely unknown factors must all be accounted for in order to provide the most accurate assessment of the health of the monitored system. Northrop Grumman Innovation Systems (NGIS) develops, produces, and provides sustainment of solid rocket motor systems for the aerospace and defense industry and has extensive experience in applying uncertainty quantification (UQ) principles to complicated numerical simulations and analyses. In this paper, lessons learned by NGIS on UQ simulations and analyses are presented, and their applicability to PHM systems is explored. Methods for measuring and tracking uncertainty through the PHM predictive train are presented, as is a Monte-Carlo-based method for performing prognostic numerical calculations, which accounts for and quantifies epistemic, ontological, and aleatory uncertainty.

The general aim of a Prognostic Health Management (PHM) system is to generate information to meet objectives such as improved mission reliability, improved safety and reduced maintenance costs. Information is derived from data (e.g. acquired by sensors) through the application of knowledge. Knowledge can be represented in different forms and can be implicit or explicit. The form of representation depends on the type of technology fulfilling some specific PHM function. PHM is complicated and successful systems will be constructed using technology from many suppliers. Careful management of technology integration is required. Proposed standards have been written to allow different technologies to communicate. But there is also a need to define formal frameworks so that technologies are integrated in a coherent manner. This paper presents some practical applications of PHM technologies that fulfil functions within different layers of the PHM process. These applications are then used to explain how these different functional technologies integrate through a common probabilistic framework.

Prognostic health management (PHM) systems are designed to predict impending faults and to determine remaining useful life of machinery. An efficient prognostic system can speed up fault diagnosis by providing an indication of what parts of the machinery or vehicle are most likely to fail and will need maintenance in the near future. In this paper, we discuss the essential steps involved in building an effective PHM system. We describe time and frequency domain features that can be extracted from raw sensor data. These features or condition indicators can help summarize the information in raw data and extract critical clues that reflect the health of the machinery. Analytical models can then be used to learn the essential health indicators and how they relate to fault conditions. In addition, we describe a case study of implementing a PHM system for a high speed face milling CNC cutter. We describe features that were analyzed from sensor data. For the analytical engine, we used a Neural Network model for learning the association of the extracted features and the magnitude of wear in the cutter. The neural network was able to determine remaining useful life of cutters in terms of number of remaining cuts for a given wear limit based on extracted features.

Electro-Mechanical Actuators (EMA) are gaining prominent roles in the next generation fly-by-wire aircraft and spacecraft. With these roles often being safety-critical (control surface or landing gear actuation, for instance), the key to faster adoption of EMA in aerospace applications is development of accurate and reliable prognostic health management (PHM) systems that not only detect and identify faults, but also predict how the identified they affect the remaining useful life (RUL) of both the faulty component and the actuator as a whole. Such information can be invaluable to pilots, controllers, and maintenance personnel in assessing how to complete or re-plan the desired mission with a sufficient safety margin. A team consisting of members of NASA Ames Diagnostic & Prognostic Group has developed a prototype PHM system for EMA that provides coverage for a number of faults modes typical to this type of actuators. The diagnostic portion of the system is implemented using a hybrid approach which utilizes both qualitative (bond graph, model-based) and quantitative (data-driven) reasoners to achieve low false positive and false negative detection rates and a high accuracy of diagnostic output. Once a fault has been diagnosed, the prognostic component, which is implemented using Gaussian Process Regression (GPR) principles, estimates the RUL of the component that is faulted. Experiments were conducted both in laboratory and flight conditions to validate the PHM system using an innovative Flyable Electromechanical Actuator (FLEA) test stand. The test stand allows experimental actuators to be subjected to environmental and operating conditions similar to what actuators on the host aircraft are experiencing, while providing researchers with the capability to safely inject and monitor propagation of various fault modes. Prognostic run-to-failure experiments were done in laboratory conditions on ballscrew jam and motor winding short faults. Flight experiments (albeit not run-to-failure) were conducted in collaboration with the US Army on UH-60 Blackhawk helicopters. The paper describes these experiments in detail and presents the results obtained from the PHM system with regard to the estimation of the RUL of the actuator.

The reliability of LED products may be affected by both luminous flux drop and color shift. Previous research on the topic focuses on either luminous maintenance or color shift. However, luminous flux degradation usually takes very long time to observe in LEDs under normal operating conditions. In this paper, the impact of a VOC (volatile organic compound) contaminated luminous flux and color stability are examined. As a result, both luminous degradation and color shift had been recorded in a short time. Test samples are white, phosphor-converted, high-power LED packages. Absolute radiant flux is measured with integrating sphere system to calculate the luminous flux. Luminous flux degradation and color shift distance were plotted versus aging time to show the degradation pattern. A prognostic health management (PHM) method based on the state variables and state estimator have been proposed in this paper. In this PHM framework, unscented kalman filter (UKF) was deployed as the carrier of all states. During the estimation process, third order dynamic transfer function was used to implement the PHM framework. Both of the luminous flux and color shift distance have been used as the state variable with the same PHM framework to exam the robustness of the method. Predicted remaining useful life is calculated at every measurement point to compare with the tested remaining useful life. The result shows that state estimator can be used as the method for the PHM of LED degradation with respect to both luminous flux and color shift distance. The prediction of remaining useful life of LED package, made by the states estimator and data driven approach, falls in the acceptable error-bounds (200%) after a short training of the estimator.

Prognostics Health Management (PHM) technology provides a solution for the transformation of nuclear power plant equipment from "planned maintenance" to "condition-based maintenance." This paper reviews recent research advances in PHM and examined fault classification method and FMECA for various types of nuclear power equipment. It also addresses key technical approaches for implementing PHM within a system engineering framework, with a focus on challenges and solutions encountered during system deployment. The study aims to guide further applied research and engineering implementation of PHM technology in the nuclear power plant. As an equipment-level intelligent system, PHM will serve as a critical foundation for achieving fully intelligent nuclear power plants.

The Prognostic Health Management (PHM) has been asserting itself as the most promising methodology to enhance the effective reliability and availability of a product or system during its life-cycle conditions by detecting current and approaching failures, thus, providing mitigation of the system risks with reduced logistics and support costs. However, PHM is at an early stage of development, it also expresses some concerns about possible shortcomings of its methods, tools, metrics and standardization. These factors have been severely restricting the applicability of PHM and its adoption by the industry. This paper presents a comprehensive literature review about the PHM main general weaknesses. Exploring the research opportunities present in some recent publications, are discussed and outlined the general guidelines for finding the answer to these issues.

We analyze the issue of decision-making using Soft Computing (SC) models. We define a natural framework in the cross product of the decision's time horizon and the type of domain knowledge used by the SC models. Within this framework, we analyze the progression from simple lexicon to annotated lexicon, morphology, syntax, semantics, and pragmatics. We compare this progression with the injection of domain knowledge in SC to perform tasks in the context of Prognostics & Health Management (PHM), such as anomaly detection and identification (unsupervised clustering), failure mode analysis (supervised learning), prognostics of remaining useful life (prediction), on-board fault accommodation (real-time control), and off board logistics actions (decision support). Finally, we analyze Evolutionary Fuzzy Systems (EFS) and determine their position and role in this framework.

The circuit fault injection based on EDA simulation is one of the efficient methods of testability analysis. In order to implement fault simulation, the first step is to establish the models that reflect the function or fault modes appropriately. Considering the various components of number systems in PHM system, although the prevalent software contains parts libraries, the symbols in the libraries and well-known modeling methods cannot meet the needs of simulation. Therefore, the analysis of integrated and practical methods of establishing function/performance models is of great necessity. In this paper, an integrated methodology of modeling is proposed. The methodology is introduced with several cases in avionics system. It provides general approach for modeling of the number systems and BITE in PHM system. The analysis of the methodology will be significant to implement the fault injection and testability analysis for PHM system.

Optimization of the Operation and Maintenance of Renewable Energy Systems by Deep Reinforcement Learning

<div class="section abstract"><div class="htmlview paragraph">Aviation industry is striving to leverage the technological advancements in connectivity, computation and data analytics. Scalable and robust connectivity enables futuristic applications like smart cabins, prognostic health management (PHM) and AI/ML based analytics for effective decision making leading to flight operational efficiency, optimized maintenance planning and aircraft downtime reduction. Wireless Sensor Networks (WSN) are gaining prominence on the aircraft for providing large scale connectivity solution that are essential for implementing various health monitoring applications like Structural Health Monitoring (SHM), Prognostic Health Management (PHM), etc. and control applications like smart lighting, smart seats, smart lavatory, etc. These applications help in improving passenger experience, flight operational efficiency, optimized maintenance planning and aircraft downtime reduction. Intra Aircraft WSNs (IAWSN) used for such applications are expected to provide robust and reliable communication performance. However, IAWSNs, when deployed, must co-exist with other wireless devices and networks based on Wi-Fi and Bluetooth technologies. Sharing of the ISM frequency band (2.4 GHz) among these networks makes the co-existence problem more challenging and has a significant impact on the Quality of Service (QoS) in terms of throughput, latency and Packet Error Rate (PER) of IAWSN. In addition, they are subjected to additional electromagnetic interference from other electronic and avionic systems onboard aircraft. There are various channel management, priority-based scheduling and time-sharing techniques that are deployed currently to address the co-existence problem. However, these methods perform a trade-off among one or more operational parameters like channel bandwidth, number of nodes per channel, throughput, latency, PER, etc. of IAWSN. Code Division Multiple Access (CDMA) technique is a proven one in cellular networks for providing reliable interference free communication performance especially in large scale networks. This paper evaluates the feasibility of deploying CDMA for IEEE 802.15.4 based IAWSN onboard aircraft. A CDMA based communication schema is proposed and simulated for IAWSN operating as per IEEE 802.15.4 protocol. The communication performance of the CDMA based IAWSN is evaluated in comparison with the performance of a standard IEEE 802.15.4 protocol implementation in the presence of interference from the co-nodes of the IAWSN and Wi-Fi devices by analyzing the QoS parameters like signal to noise ratio (SNR), operational bandwidth, Bit-Error Rate (BER) and Process Gain through simulation. Simulations results are evaluated against the desired performance level for a reference use case application. Improvement opportunities are identified and areas for future research are proposed.</div></div>

This paper describes the basic concepts of the PROMIS methodology for system's Prognostic Health Management (PHM). The key elements of the PROMIS methodology are: (i) system health is monitored by continuously measuring so-called Condition Parameters (CPs), representing the physical condition of critical system components, (ii) CPs are derived using model-based estimation processes so that the existing set of sensors is exploited as much as possible, and (iii) the remaining lifetime of components is calculated using aging models and the expected system load.

The application of Prognostic Health Management (PHM) methods offer the potential of improved reliability in the maintenance of large, remote and complex assets ensuring they continue to achieve optimal performance throughout their designed lifetime. This paper reviews the current state-of-the-art in condition monitoring for the offshore renewable energy sector to highlight the current capabilities and limitations. Based upon this review and an examination of PHM case studies, the design and function of PHM systems for offshore renewable industry assets will be presented.

Escalation of the expense of gold has resulted in industry interest in use of copper as alternative wire bonds interconnect material. Copper wire has the advantage of lower price and comparable electrical resistance to gold wire. In this paper, 32-pin copper-aluminum wire bond chip scale packages are aged at three types of environment conditions separately. Environmental conditions included: 200°C for 10 days, 85°C and 85% RH for 8 weeks and −40°C to 125°C for 500 thermal cycles. The resistances of the wire bond are obtained every 24 hours for 200°C environment, every 7 days for 85C/85RH environment and every 5 days (50 thermal cycles) for the thermal cycling environment. A leading indicator has been developed in order to monitor the progression effect of the different thermal aging condition on the package and prognosticate remaining useful life based on the resistance spectroscopy. The Cu-Al wire bond resistance has been measured using a modified Wheatstone bridge. It has been shown previously that precise resistance spectroscopy is able to offer the failure of a leading indicator prior to the traditional definition of failure. The prognostic health management is qualified to be an efficient and accuracy tool for assessment of the remaining life of the wire bond. The ability to predict the remaining useful life of Cu-Al wire bond provides several advantages, including increasing safety by providing warning ahead of time before the failure.

The development of light-emitting diode (LED) technology has resulted in widespread solid state lighting (SSL) use in consumer and industrial applications. Previous researchers have shown that LEDs from the same manufacturer and operating under same use-condition may have significantly different degradation behavior. Applications of LEDs to safety critical and harsh environment applications necessitate the characterization of failure mechanisms and modes. This paper focuses on a prognostic health management (PHM) method based on the measurement of forward voltage and forward current of bare LED under harsh environment. In this paper, experiments have been done on single LEDs subjected to combined temperature–humidity environment of 85 °C, 85% relative humidity. Pulse width modulation (PWM) control method has been employed to drive the bare LED in order to reduce the heat effect caused by forward current and high frequency (300 Hz). A data acquisition system has been used to measure the peak forward voltage and forward current. Test to failure (luminous flux drops to 70%) data has been measured to study the effects of high temperature and humid environment loadings on the bare LEDs. A solid state cooling method with a Peltier cooler has been used to control the temperature of the LED in the integrating sphere when taking the measurement of luminous flux. The shift of forward voltage forward current curve and lumen degradation has been recorded to help build the failure model and predict the remaining useful life (RUL). Particle filter has been employed to assess the RUL of the bare LED. Model predictions of RUL have been correlated with experimental data. Results show that prediction of RUL of LEDs, made by the particle filter model, works with acceptable error-bounds. The presented method can be employed to predict the failure of LED caused by thermal and humid stresses.