Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network

Deep learning approach for active classification of electrocardiogram signals

(2016) M.M. Al Rahhal et al.   INFORMATION SCIENCES

A sparse auto-encoder-based deep neural network approach for induction motor faults classification

(2016) Wenjun Sun et al.   MEASUREMENT

Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings

(2016) Meng Gan et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

(2016) Feng Jia et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Vibration Spectrum Imaging: A Novel Bearing Fault Classification Approach

(2015) Muhammad Amar et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection

(2015) Liangwei Zhang et al.   RELIABILITY ENGINEERING & SYSTEM SAFETY

Anomaly Detection Based on Sensor Data in Petroleum Industry Applications

(2015) Luis Martí et al.   SENSORS

Trends in big data analytics

(2014) Karthik Kambatla et al.   JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING

Data-driven soft sensor development based on deep learning technique

(2014) Chao Shang et al.   JOURNAL OF PROCESS CONTROL

From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis

(2013) Xuewu Dai et al.   IEEE Transactions on Industrial Informatics

A Rank-Based Approach to Active Diagnosis

(2013) G. Bellala et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines

(2013) Xiaoyuan Zhang et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Failure diagnosis using deep belief learning based health state classification

(2013) Prasanna Tamilselvan et al.   RELIABILITY ENGINEERING & SYSTEM SAFETY

A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization

(2012) A. Azadeh et al.   APPLIED SOFT COMPUTING

Comparison between intensity normalization techniques for dynamic susceptibility contrast (DSC)-MRI estimates of cerebral blood volume (CBV) in human gliomas

(2012) Benjamin M. Ellingson et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

An effective procedure exploiting unlabeled data to build monitoring system

(2011) Xiukuan Zhao et al.   EXPERT SYSTEMS WITH APPLICATIONS

Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network

(2011) G.F. Bin et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Using active learning to adapt remote sensing image classifiers

(2011) D. Tuia et al.   REMOTE SENSING OF ENVIRONMENT

Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee–Eastman process

(2010) Reza Eslamloueyan   APPLIED SOFT COMPUTING

A semi-supervised approach to fault diagnosis for chemical processes

(2010) Isaac Monroy et al.   COMPUTERS & CHEMICAL ENGINEERING

Fault detection and diagnosis in process data using one-class support vector machines

(2009) Sankar Mahadevan et al.   JOURNAL OF PROCESS CONTROL

Performance assessment of a novel fault diagnosis system based on support vector machines

(2008) Ignacio Yélamos et al.   COMPUTERS & CHEMICAL ENGINEERING

Model-based monitoring and fault diagnosis of fossil power plant process units using Group Method of Data Handling

(2008) Fan Li et al.   ISA TRANSACTIONS