Douglas W. Brown

Prognostic health management for avionics system power supplies

This paper presents an integrated approach to switching mode power supply health management that implements techniques from engineering disciplines including statistical reliability modeling, damage accumulation models, physics of failure modeling, and sensor-based condition monitoring using automated reasoning algorithms. Novel features extracted from sensed parameters such as temperature, power quality, and efficiency were analyzed using advanced fault detection and damage accumulation algorithms. Using model-based assessments in the absence of fault indications, and updating the model-based assessments with sensed information when it becomes available provides health state awareness at any point in time. Intelligent fusion of this diagnostic information with historical component reliability statistics provides a robust health state awareness as the basis for accurate prognostic predictions. Complementary prognostic techniques including analysis of projected operating conditions by physics-based component aging models, empirical (trending) models, and system level failure progression models will be used to develop verifiable prognostic models. The diagnostic techniques, and prognostic models have been demonstrated through accelerated failure testing of switching mode power supplies

Turn-Off Time as an Early Indicator of Insulated Gate Bipolar Transistor Latch-up

In this paper, effects preceding a latch-up fault in insulated gate bipolar transistors (IGBTs) are studied. Primary failure modes associated with IGBT latch-up faults are reviewed. Precursors to latch-up, primarily an increase in turn-off time as a consequence of elevated junction temperature, are examined for an IGBT. The relationship between junction temperature and turn-off time is explained by modeling the parasitic properties of an IGBT. A metric is derived from the model to standardize the relative estimates in junction temperature from measurements of turn-off time. To evaluate the effects preceding latch-up in-situ, seeded fault testing is conducted on a three-phase power inverter using aged transistors induced with a fault located in the die-attach solder layer. Experimental results demonstrated the feasibility of using the proposed metric as a precursor to transistor latch-up.

Electronic prognostics ¿ A case study using global positioning system (GPS).

Prognostic health management (PHM) of electronic systems presents challenges traditionally viewed as either insurmountable or otherwise not worth the cost of pursuit. Recent changes in weapons platform acquisition and support requirements has spurred renewed interest in electronics PHM, revealing possible applications, accessible data sources, and previously unexplored predictive techniques. The approach, development, and validation of electronic prognostics for a radiofrequency (RF) system are discussed in this paper. Conventional PHM concepts are refined to develop a three-tier failure mode and effects analysis (FMEA). The proposed method identifies prognostic features by performing device, circuit, and system-level modeling. Accelerated failure testing validates the identified diagnostic features. The results of the accelerated failure tests accurately predict the remaining useful life of a COTS GPS receiver to within plusmn5 thermal cycles. The solution has applicability to a broad class of mixed digital/analog circuitry, including radar and software defined radio

Online Ringing Characterization as a Diagnostic Technique for IGBTs in Power Drives

Embeddable features that are easily incorporated in traditional power drive systems are identified for prognostics and health management (PHM) systems. The proposed novel feature takes advantage of the original pulsewidth modulation (PWM) waveform produced by the inverter that is already available in the system as a succession of step functions to study the systems response at high frequencies. The high-order oscillatory responses (ringing) present in the voltages and currents of the system are a reflection of the interaction among the internal parametric components of the power devices, allowing device characterization. Evaluating the change over time of these parameters characterized from ringing becomes a key novel feature to assess the aging status of the power electronic circuit and electric machine with respect to transistor degradation. We propose the use of a low-cost bandpass analog filter centered at a high frequency, which is relevant as a feature input capable of aging tracking. The simplified model supporting ringing as a feature to evaluate component aging and its experimental evaluation are presented with experimental data, corroborating its viability as a practical real-time power device health-state indicator.

An integrated architecture for fault diagnosis and failure prognosis of complex engineering systems

Highlights? .NET framework as foundation of the software architecture. ? Probabilistic method called particle filtering using Bayesian estimates perform diagnostics and prognostics. ? Real world applications show the advantages of the architecture. Complex engineering systems, such as aircraft, industrial processes, and transportation systems, are experiencing a paradigm shift in the way they are operated and maintained. Instead of traditional scheduled or breakdown maintenance practices, they are maintained on the basis of their current state/condition. Condition-Based Maintenance (CBM) is becoming the preferred practice since it improves significantly the reliability, safety and availability of these critical systems. CBM enabling technologies include sensing and monitoring, information processing, fault diagnosis and failure prognosis algorithms that are capable of detecting accurately and in a timely manner incipient failures and predicting the remaining useful life of failing components. If such technologies are to be implemented on-line and in real-time, it is essential that an integrating system architecture be developed that possesses features of modularity, flexibility and interoperability while exhibiting attributes of computational efficiency for both on-line and off-line applications. This paper presents a .NET framework as the integrating software platform linking all constituent modules of the fault diagnosis and failure prognosis architecture. The inherent characteristics of the .NET framework provide the proposed system with a generic architecture for fault diagnosis and failure prognosis for a variety of applications. Functioning as data processing, feature extraction, fault diagnosis and failure prognosis, the corresponding modules in the system are built as .NET components that are developed separately and independently in any of the .NET languages. With the use of Bayesian estimation theory, a generic particle-filtering-based framework is integrated in the system for fault diagnosis and failure prognosis. The system is tested in two different applications-bearing spalling fault diagnosis and failure prognosis and brushless DC motor turn-to-turn winding fault diagnosis. The results suggest that the system is capable of meeting performance requirements specified by both the developer and the user for a variety of engineering systems.

Particle filter based anomaly detection for aircraft actuator systems

This paper describes the background, simulation and experimental evaluation of an anomaly detector for Brushless DC motor winding insulation faults in the context of an aircraft Electro-Mechanical Actuator (EMA) application. Results acquired from an internal Failure Modes and Effects Analysis (FMEA) study identified turn-to-turn winding faults as the primary mechanism, or mode, of failure. Physics-of-failure mechanisms used to develop a model for the identified fault are provided. The model was implemented in Simulink to simulate the dynamics of the motor with a turn-to-turn insulation winding fault. Then, an experimental test procedure was devised and executed to validate the model. Additionally, a diagnostic feature, identified by the fault model and derived using Hilbert transforms, was validated using the Simulink model and experimental data for several fault dimensions. Next, a feature extraction routine preprocesses monitoring parameters and passes the resulting features to a particle filter. The particle filter, based on Bayesian estimation theory, allows for representation and management of uncertainty in a computationally efficient manner. The resulting anomaly detection routine declares a fault only when a specified confidence level is reached at a given false alarm rate. Finally, the real-time performance of the anomaly detector is evaluated using LabVIEW.

Electronic prognostics - a case study using Global Positioning System (GPS)

Prognostic health management (PHM) of electronic systems presents challenges traditionally viewed as either insurmountable or otherwise not worth the cost of pursuit. Recent changes in weapons platform acquisition and support requirements has spurred renewed interest in electronics PHM, revealing possible applications, accessible data sources, and previously unexplored predictive techniques. The approach, development, and validation of electronic prognostics for a radiofrequency (RF) system are discussed in this paper. Conventional PHM concepts are refined to develop a three-tier failure mode and effects analysis (FMEA). The proposed method identifies prognostic features by performing device, circuit, and system-level modeling. Accelerated failure testing validates the identified diagnostic features. The results of the accelerated failure tests accurately predict the remaining useful life of a COTS GPS receiver to within plusmn5 thermal cycles. The solution has applicability to a broad class of mixed digital/analog circuitry, including radar and software defined radio

Prognostic health management for avionic systems

Maintenance of aircraft electronic systems has traditionally been performed in reaction to reported failures or through periodic system replacements. Recent changes in weapons platform acquisition and support requirements have spurred interest in application of prognostic health management (PHM) concepts developed for mechanical systems to electronic systems. The approach, development, and validation of prognostics for two types of electronic equipment are discussed in this paper. The two applications, a switch-mode power supply and a GPS receiver were selected based on their relatively high failure rates and relevance to many commonly used avionics systems. The method identifies prognostic features by performing device, circuit, and system-level modeling. Device modeling with equivalent circuit and mathematical physics of failure models describe parameter degradation resulting from damage accumulation for each device. Prognostic features extracted from a small array of sensors on the power supply, and from the GPS operational communication data stream are used to update life usage and failure progression models to provide an indication of the health state. The results of accelerated failure tests on both systems are used to illustrate the approach and demonstrate its effectiveness in predicting the useful life remaining. The solutions have applicability to power supplies in many avionic systems, and to a broad class of mixed digital/analog circuitry including radar and software defined radio

Rolling element bearing feature extraction and anomaly detection based on vibration monitoring

In this paper, an anomaly detection structure, in which different types of anomaly detection routines can be applied, is proposed. Bearing fault modes and their effects on the bearing vibration are discussed. Based on this, a feature extraction method is developed to overcome the limitation of time domain features. Experimental data from bearings under different operating conditions are used to verify the proposed method. The results show that the extracted feature has a monotonic decrease trend as the dimension of fault increases. The feature also has the ability to compensate the variation of rotating speed. The proposed structure are verified with three different detection routines, pdf-based, k-nearest neighbor, and particle-filter-based approaches.

On-line ringing characterization as a PHM technique for power drives and electrical machinery

Embeddable features easily incorporated in traditional power drive systems are identified for prognostic health management (PHM) systems. This novel technique utilizes the original PWM waveform produced by the inverter to evaluate the power electronic circuit and electric machine against transistor degradation. Evaluation of the primary feature, ringing characterization, with experimental data demonstrates its viability as a practical real-time power device health-state indicator.