This paper introduces an on-line particle-filtering (PF)-based framework for fault diagnosis and failure prognosis in non-linear, non-Gaussian systems. considers the implementation of two autonomous modules. A detection identification (FDI) module uses a hybrid state-space model plant PF algorithm to estimate state probability density function (pdf) system calculates condition real-time. Once anomalous is detected, available pdf estimates are used as initial conditions prognostic routines....

This paper introduces a method to detect fault associated with critical components/subsystems of an engineered system. It is required, in this case, the condition as early possible, specified degree confidence and prescribed false alarm rate. Innovative features enabling technologies include Bayesian estimation algorithm called particle filtering, which employs or indicators derived from sensor data combination simple models system's degrading state deviation discrepancy between baseline...

Fault diagnosis and prognosis are some of the most crucial functionalities in complex safety-critical engineering systems, particularly fault diagnosis, has been a subject intensive research past four decades. Such capabilities allow for detection isolation early developing faults as well prediction propagation, which can preventive maintenance, or even serve countermeasure to possibility catastrophic incidence result failure. Following short preliminary overview definitions, this article...

Machine prognosis is a significant part of condition-based maintenance and intends to monitor track the time evolution fault so that can be performed or task terminated avoid catastrophic failure. A new prognostic method developed in this paper using adaptive neuro-fuzzy inference systems (ANFISs) high-order particle filtering. The ANFIS trained via machine historical failure data. its modeling noise constitute an <i xmlns:mml="http://www.w3.org/1998/Math/MathML"...

This paper presents the implementation of a particle-filtering-based prognostic framework that allows estimating state health (SOH) and predicting remaining useful life (RUL) energy storage devices, more specifically lithium-ion batteries, while simultaneously detecting isolating effect self-recharge phenomena within life-cycle model. The proposed scheme statistical characterization capacity regeneration are validated through experimental data from an accelerated battery degradation test set...

This paper presents a class of risk measures to be used as damage indicators within particle filtering (PF)-based real-time prognosis algorithms, with application the case state-of-charge prediction in lithium-ion batteries. The proposed measure not only incorporates battery failure but also is for confidence on algorithm itself. In addition, novel simplified PF-based prognostic method estimate discharge time, while providing computationally inexpensive solution. Computing times both routine...

In an electricity market environment, energy storage plant owners are remunerated for the provision of services to multiple sectors. Some these services, however, may accelerate battery aging and degradation hence this needs be properly balanced against associated remunerations. framework, we propose a combined economic-degradation model quantify effects operational policies (mainly focused on constraining State Charge -SOC- prescribed levels in order reduce aging) gross revenue,...

We present the implementation of a particle-filteringbased prognostic framework that utilizes statistical characterization use profiles to (i) estimate state-of-charge (SOC), and (ii) predict discharge time energy storage devices (lithium-ion batteries).The proposed approach uses novel empirical statespace model, inspired by battery phenomenology, particle-filtering algorithms SOC other unknown model parameters in real-time.The adaptation mechanism used during filtering stage improves...

Aircraft are complex engineering systems composed of many interconnected subsystems with possible uncertainties in their structure. They often function for a long number flight hours under varying or harsh environments. Hence, prognostic and health management (PHM) critical components within the overall system is crucial maintaining safety reliability aircraft. This article reviews state art aircraft failure prognostic. The main definitions concepts presented discussed. In addition, selected...

Particle filters (PF) have been established as the de facto state of art in failure prognosis. They combine advantages rigors Bayesian estimation to nonlinear prediction while also providing uncertainty estimates with a given solution. Within context particle filters, this paper introduces several novel methods for representations and management. The is modeled via rescaled Epanechnikov kernel assisted resampling techniques regularization algorithms. Uncertainty management accomplished...

Temperature prediction of a battery plays significant role in terms energy efficiency and safety electric vehicles, as well several kinds electronic devices. In this regard, it is crucial to identify an adequate model study the thermal behavior battery. This article reports comparative on modeling approaches by using LiCoO2 26650 lithium-ion battery, provides methodology characterize electrothermal phenomena. Three have been implemented numerically—a lumped model, 3D computational fluid...

The failure progression of wind turbine bearings comprises multiple degraded health states due to applied load by varying operating conditions (VOC). Therefore, determining the VOC impact on dynamics severity is an essential task for bearing prognostics. This article introduces a hybrid prognosis method using real-time supervisory control and data acquisition (SCADA) vibration signals predict remaining useful life (RUL) bearings. SCADA are utilized define role environmental such as speed...

This paper presents the implementation of an online particle-filtering-based framework for fault diagnosis and failure prognosis in a turbine engine. The methodology considers two autonomous modules, assumes existence indicators (for monitoring purposes) availability real-time measurements. A detection identification (FDI) module uses hybrid state-space model plant, particle filtering algorithm to calculate probability crack one blades turbine; simultaneously computing state density function...

This paper introduces an on-line particle-filtering-based framework for failure prognosis in nonlinear, non-Gaussian systems. uses a nonlinear state-space model of the plant(with unknown time-varying parameters) and particle filtering(PF) algorithm to estimate probability density function(pdf) state real-time. The pdf is then used predict evolution time fault indicator, obtaining as result remaining useful life(RUL) faulty subsystem. approach provides information about precision accuracy...