Kai Goebel

Palo Alto Research Center | PARC · Intelligent Systems Lab

Prognostic Health Management aims to predict the Remaining Useful Life (RUL) of degrading components/systems utilizing monitoring data. These RUL predictions form the basis for optimizing maintenance planning in a Predictive Maintenance (PdM) paradigm. We here propose a metric for assessing data-driven prognostic algorithms based on their impact on...

To ensure resilience, systems must be endowed with capabilities for rapid detection, response, and recovery to disruptiveevents. In this paper we focus on faults as disruptive events and use a diagnosis engine for their detection and isolation. In particular we use model-based diagnosis, where the diagnosis engine is provided with a model of the sy...

The real-time, and accurate inference of model parameters is of great importance in many scientific and engineering disciplines that use computational models (such as a digital twin) for the analysis and prediction of complex physical processes. However, fast and accurate inference for processes of complex systems cannot easily be achieved in real-...

Maintenance decisions in domains such as aeronautics are becoming increasingly dependent on being able to predict the failure of components and systems. When data-driven techniques are used for this prognostic task, they often face headwinds due to their perceived lack of interpretability. To address this issue, this paper examines how features use...

Gearbox fault diagnosis is expected to significantly improve the reliability, safety and efficiency of power transmission systems. However, planetary gearbox fault diagnosis remains a challenge due to complex responses caused by multiple planetary gears. Model-based gearbox fault diagnosis techniques extract hand-crafted features from sensor data b...

Physics-based and data-driven models for remaining useful lifetime (RUL) prediction typically suffer from two major challenges that limit their applicability to complex real-world domains: (1) the incompleteness of physics-based models and (2) the limited representativeness of the training dataset for data-driven models. Combining the advantages of...

A Bayesian hierarchical model (BHM) is developed to predict bearing life using envelope acceleration data in combination with a degradation model and prior knowledge of the bearing rating life. The BHM enables the inference of individual bearings, groups of bearings, or bearings operating under certain conditions. The key benefit of the BHM approac...

To achieve system resilience, one can leverage high-level design features (e.g., redundancies and fail-safes), adjust operational profiles (e.g., load or trajectory), and use appropriate contingency management (e.g., emergency procedures) to mitigate potential hazards. For example, in the design of a novel drone, one would optimize the rotor and ba...

When the influence of changing operational and environmental conditions is not factored out, it can be dificult to observe a clear deterioration path. This can significantly affect the task of prognostics and other analytic operations. To address this issue, it is necessary to baseline the data, typically by first finding the operating regimes and...

Incorporating resilience in design is important for the long-term viability of complex engineered systems. Complex aerospace systems, for example, must ensure safety in the event of hazards resulting from part failures and external circumstances while maintaining efficient operations. Traditionally, mitigating hazards in early design has involved e...

Prognostics and health management (PHM) of bearings is crucial for reducing the risk of failure and the cost of maintenance for rotating machinery. Model-based prognostic methods develop closed-form mathematical models based on underlying physics. However, the physics of complex bearing failures under varying operating conditions is not well unders...

As the field of PHM matures, it needs to be aware of the regulations, policies, and standards that will both impose boundaries as well as provide guidance for operations. All three - regulations, policies, and standards - provide information on how to design or operate something, but with different degrees of enforceability. Policies include both p...

Prognostics and health management (PHM) is becoming one of the most popular topics for research and development in the aviation industry. The reasons for this are varied, but one of the main ones is that PHM affords the operator with a way to reduce lifecycle operating costs without necessarily adding expensive accessories that might need to be cer...

A key enabler of intelligent maintenance systems is the ability to predict the remaining useful lifetime (RUL) of its components, i.e., prognostics. The development of data-driven prognostics models requires datasets with run-to-failure trajectories. However, large representative run-to-failure datasets are often unavailable in real applications be...

Prognostic information is used to make decisions such as when to perform maintenance or - in time sensitive and safety critical applications - when to change operational settings. Where distributions about expected end of life (EOL) are available, these decisions are often based on risk-informed thresholds, for example a 2-sigma or 3-sigma criterio...

Incorporating resilience in design is important for the long-term viability of complex engineered systems. Complex aerospace systems, for example, must ensure safety in the event of hazards resulting from part failures and external circumstances while maintaining efficient operations. Traditionally, mitigating hazards in early design has involved e...

Resilience models assess a system's ability to withstand disruption by quantifying the value of metrics (e.g. expected cost or loss) over time. When such a metric is the result of injecting faults in a dynamic model over an interval of time, it is important that it represent the statistical expectation of fault responses rather than a single respon...

Prior to failure, most systems exhibit signs of changed characteristics. The early detection of this change is important to remaining useful life estimation. To have the ability to detect the inflection point or ''elbow point" of an asset, i.e. the point of the degradation curve that marks the transition from nominal to faulty condition, can enable...

The dynamic, real-time, and accurate inference of model parameters from empirical data is of great importance in many scientific and engineering disciplines that use computational models (such as a digital twin) for the analysis and prediction of complex physical processes. However, fast and accurate inference for processes with large and high dime...

A number of risk and resilience-based design methods have been put forward over the years that seek to provide designers the tools to reduce the effects of potential hazards in the early design phase. However, because of the associated high level of uncertainty and low-fidelity design representations, one might justifiably wonder if using a resilie...

With the increased availability of condition monitoring data on the one hand and the increased complexity of explicit system physics-based models on the other hand, the application of data-driven approaches for fault detection and isolation has recently grown. While detection accuracy of such approaches is generally very good, their performance on...

Accurately estimating the time of battery End of Discharge (EOD) in electric Unmanned Aerial Vehicles (UAVs) provides assurance that a given mission can be completed before the energy stored in the battery runs out, and aids decision-making processes such as mission replanning to mitigate shortcomings associated with the available energy. The accur...

Data-driven and physics-based models for remaining useful lifetime (RUL) prediction typically suffer from two major challenges that limit their applicability to complex real-world domains: (a) high complexity or incompleteness of physics-based models and (b) limited representativeness of the training dataset for data-driven models. The work propose...

The SAE International Journal of Aerospace is the preeminent source for peer-reviewed, cutting-edge engineering research within the aerospace industry. The journal is an essential resource for anyone in academia, industry, or government seeking the latest studies and technology in aerospace engineering. In addition to being identified as some of th...

A novel method to determine probabilistic operational safety bound for rotary-wing unmanned aircraft systems (UAS) traffic management is proposed in this paper. The key idea is to combine a deterministic model for rotary-wing UAS flying distance estimation to avoid conflict and a probabilistic uncertainty quantification methodology to evaluate the...

The Single-Particle Model (SPM) of Li ion cell \cite{Santhanagopalan06, Guo2011} is a computationally efficient and fairly accurate model for simulating Li ion cell cycling behavior at weak to moderate currents. The model depends on a large number of parameters describing the geometry and material properties of a cell components. In order to use th...

Prognostics and Health Management (PHM) systems have been shown to provide many benefits to the reliability, performance , and life of engineered systems. However, because of trade-offs between up-front design and implementation costs, operational performance, and reliability, it may not be obvious in the early design phase whether one PHM system w...

Estimating accurate Time-of-Failure (ToF) of a system is key in making the decisions that impact operational safety and optimize cost. In this context, it is interesting to note that different approaches have been explored to tackle the problem of estimating ToF. The difference is in part characterized by different definitions of the hazard zones....

This paper proposed a novel method to determine probabilistic operational safety bound for unmanned aircraft traffic management. The key idea is to implement probabilistic uncertainty quantification and design the operational safety bound shape considering UAV’s heading direction. Operational safety bound is used to identify a virtual geographic bo...

Evacuating residents out of affected areas is an important strategy for mitigating the impact of natural disasters. However, the resulting abrupt increase in the travel demand during evacuation causes severe congestions across the transportation system, which thereby interrupts other commuters' regular activities. In this paper, a bi-level mathemat...

Due to the expansive, time-consuming nature of risk analyses, it is important to be able to assign the minimization of risk (and, in general, optimization of resilience) to responsible teams that can work in parallel. However, while methods exist for minimization of risk in conventional design processes, research has not yet shown how it should be...

With the increased availability of condition monitoring data on the one hand and the increased complexity of explicit system physics-based models on the other hand, the application of data-driven approaches for fault detection and isolation has recently grown. While detection accuracy of such approaches is generally very good, their performance on...

The role of prognostics and health management is ever more prevalent with advanced techniques of estimation methods. However, data processing and remaining useful life prediction algorithms are often very different. Some difficulties in accurate prediction can be tackled by redefining raw data parameters into more meaningful and comprehensive healt...

This article presents a holistic framework for the design, implementation and experimental validation of Battery Management Systems (BMS) in rotatory-wing Unmanned Aerial Vehicles (UAVs) that allows to accurately (i) estimate the State of Charge (SOC), and (ii) predict the End of Discharge (EOD) time of lithium-polymer batteries in small-size multi...

Prognostic performance is associated with accurately estimating remaining useful life. Difficulty in accurate prognostic applications can be tackled by processing raw sensor readings into more meaningful and comprehensive health condition indicators that will then provide performance information for remaining useful life estimations. To that end, t...

As we enter an era where intelligent systems are omnipresent and where they also permeate Prognostics and Health Management (PHM), the discussion of moral machines or ethics in engineering will inevitably engulf PHM as well. This article explores the topic of ethics within the PHM domain: how it is relevant, and how it may be addressed in a conscie...

As a system becomes more complex, the uncertainty in the operating conditions increases. In such a system, implementing a precise failure analysis in early design stage is vital. However, there is a lack of applicable methodology that shows how to implement failure analysis in the early design phase to achieve a robust design. The main purpose of t...

In this data article, a reconstructed database, which provides information from PHM08 challenge data set, is presented. The original turbofan engine data were from the Prognostic Center of Excellence (PCoE) of NASA Ames Research Center (Saxena and Goebel, 2008), and were simulated by the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS...

Degradation modeling and prediction of remaining useful life (RUL) are crucial to prognostics and health management of aircraft engines. While model-based methods have been introduced to predict the RUL of aircraft engines, little research has been reported on estimating the RUL of aircraft engines using novel data-driven predictive modeling method...

Complex engineered systems can carry risk of high failure consequences, and it is desirable for complex engineered systems to be resilient such that they can avoid or quickly recover from faults. Ideally, this should be done at the early design stage where designers are most able to explore a large space of concepts. Previous work has shown that fu...

Electric Unmanned Aerial Vehicles (UAVs) experience problems and risks associated with battery aging and abuse effects. Therefore, a Battery Health Management (BHM) system is necessary to make the battery a safe, reliable, and cost-efficient solution. BHM systems are essential to ensure that the mission goal(s) can be achieved and to aid in online...

Expanding Prognostics and Health Management (PHM) from an equipment-centric view to complex large-scale engineering systems is a challenging problem. One example for a large engineering system is the next generation national airspace system (NAS), which is a fully coupled cyberphysical- human system. This paper presents an overview of a NASA Univer...

Electric Unmanned Aerial Vehicles (UAVs) experience problems and risks associated with battery aging and abuse effects. Therefore, a Battery Health Management (BHM) system is necessary to make the battery a safe, reliable, and cost-efficient solution. BHM systems are essential to ensure that the mission goal(s) can be achieved and to aid in online...

Complex engineered systems are often associated with risk due to high failure consequences, high complexity, and large investments. As a result, it is desirable for complex engineered systems to be resilient such that they can avoid or quickly recover from faults. Ideally, this should be done at the early design stage where designers are most able...

In design process of a complex engineered system, studying the behavior of the system prior to manufacturing plays a key role to reduce cost of design and enhance the efficiency of the system during its lifecycle. To study the behavior of the system in the early design phase, it is required to model the characterization of the system and simulate t...

In deep space manned travels, the crew life will be totally dependent on the environment control and life support system of the spacecraft. A life-support system for manned missions is a set of technologies to regenerate the basic life-support elements, such as oxygen and water, which makes resilience a paramount feature of this system. The resilie...

The conceptual design phase is the first step in the design process of an engineering system. Most engineering systems, including cogeneration plants, may and likely will experience some malfunctions during its life cycle. The metrics typically considered in the conceptual design phase (and for analysis and optimization) of energy systems are cost,...

This paper introduces an approach to quantify resilience for the design of systems that can be described as a network. A key characteristic of resilience is the ability of restoring functionality and performance in response to a disruptive event. Therefore, the restoration behavior is encapsulated via a non-linear function that provides the ability...

This chapter summarises what has been developed and indicates the next steps of research, essential developments and how our approach using active system control (ASC) can be applied to aircraft design.

The use of Echo State Networks (ESNs) for the prediction of the Remaining Useful Life (RUL) of industrial components, i.e. the time left before the equipment will stop fulfilling its functions, is attractive because of their capability of handling the system dynamic behavior, the measurement noise, and the stochasticity of the degradation process....

This paper proposes a particle filter-based Bayesian framework for damage prognosis of composite laminates exhibiting concurrent matrix cracks and delamination. Literature shows a number of applications of particle filtering for real-time prognosis of metallic structures and, recently, matrix crack density evolution in composites. The work presente...

Prognostics is a systems engineering discipline focused on predicting end-of-life of components and systems. As a relatively new and emerging technology, there are few fielded implementations of prognostics, due in part to practitioners perceiving a large hurdle in developing the models, algorithms, architecture, and integration pieces. Similarly,...

This paper presents a framework to compare the resiliency of different designs during the conceptual design, when information about implementation details is unavailable. We apply the Inherent Behavioral Functional Model (IBFM) tool to develop an initial functional model for a system and simulate the failure behavior. The simulated failure scenario...

This work presents an efficient computational framework for prognostics by combining the particle filter-based prognostics principles with the technique of Subset Simulation, first developed in S.K. Au and J.L. Beck [Probabilistic Engrg. Mech., 16 (2001), pp. 263-277], which has been named PFP-SubSim. The idea behind PFP-SubSim algorithm is to spli...

Prognostics is the science of making predictions of engineering systems. It is part of a suite of techniques that determine whether a system is behaving within nominal operational bounds and – if it does not – that determine what is wrong and how long it will take until the system no longer fulfills certain functional requirements. This book presen...

Book can be found on amazon (http://a.co/0xXzmJ6) Prognostics is the science of making predictions of engineering systems. It is part of a suite of techniques that determine whether a system is behaving within nominal operational bounds and – if it does not – that determine what is wrong and how long it will take until the system no longer fulfill...

Technological advancements in real-time distributed sensing and processing for structural health monitoring systems have enabled exploration of the next frontier in structural health monitoring for in-situ condition-based prediction of remaining life of damaged or aging structures. In that context, model-based prognostics methods have shown conside...

Because valves control many critical operations, they are prime candidates for deployment of prognostic algorithms. But, similar to the situation with most other components, examples of failures experienced in the field are hard to come by. This lack of data impacts the ability to test and validate prognostic algorithms. A solution sometimes employ...