Incipient Fault Detection in a Hydraulic System Using Canonical Variable Analysis Combined with Adaptive Kernel Density Estimation
نویسندگان
چکیده
Incipient fault detection in a hydraulic system is challenge the condition monitoring community. Existing research mainly monitors abnormal working conditions systems by separately detecting key parameter, which often causes high miss warning rate for incipient faults due to oversight of parameter dependence. A principal component analysis provides an effective method taking correlation multiple parameters into consideration, but this technique assumes are Gaussian-distributed, making it invalid dynamic non-Gaussian system. In paper, we combine canonical variable (CVA) and adaptive kernel density estimation (AKDE) early nonlinear systems. The collected data set was used construct typical space, state space residual divided represent characteristics different correlations between two variables, quantitatively described using Hotelling’s T2 Q. order investigate proper upper control limits, AKDE utilised estimate underlying probability functions Q nonlinearity variables consideration. advantages proposed approach illustrated via marine power plant lubrication
منابع مشابه
Adaptive kernel density estimation
This insert describes the module akdensity. akdensity extends the official kdensity that estimates density functions by the kernel method. The extensions are of two types: akdensity allows the use of an “adaptive kernel” approach with varying, rather than fixed, bandwidths; and akdensity estimates pointwise variability bands around the estimated density functions.
متن کاملStator Fault Detection in Induction Machines by Parameter Estimation Using Adaptive Kalman Filter
This paper presents a parametric low differential order model, suitable for mathematically analysis for Induction Machines with faulty stator. An adaptive Kalman filter is proposed for recursively estimating the states and parameters of continuous–time model with discrete measurements for fault detection ends. Typical motor faults as interturn short circuit and increased winding resistance ...
متن کاملA variable bandwidth selector in multivariate kernel density estimation
Based on a random sample of size n from an unknown d-dimensional density f , the problem of selecting the variable (or adaptive) bandwidth in kernel estimation of f is investigated. The common strategy is to express the variable bandwidth at each observation as the product of a local bandwidth factor and a global smoothing parameter. For selecting the local bandwidth factor a method based on cl...
متن کاملKernel Density Estimation for An Anomaly Based Intrusion Detection System
This paper presents a new nonparametric method to simulate probability density functions of some random variables raised in characterizing an anomaly based intrusion detection system (ABIDS). A group of kernel density estimators is constructed and the criterions for bandwidth selection are discussed. In addition, statistical parameters of these distributions are computed, which can be used dire...
متن کاملCAKE: Convex Adaptive Kernel Density Estimation
In this paper we present a generalization of kernel density estimation called Convex Adaptive Kernel Density Estimation (CAKE) that replaces single bandwidth selection by a convex aggregation of kernels at all scales, where the convex aggregation is allowed to vary from one training point to another, treating the fundamental problem of heterogeneous smoothness in a novel way. Learning the CAKE ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Sensors
سال: 2023
ISSN: ['1424-8220']
DOI: https://doi.org/10.3390/s23198096