Machine health surveillance system by using deep learning sparse autoencoder
Deep learning is a rapidly growing research area having state of art achievement in various
applications including but not limited to speech recognition, object recognition, machine …
applications including but not limited to speech recognition, object recognition, machine …
Health monitoring and fault prediction using a lightweight deep convolutional neural network optimized by Levy flight optimization algorithm
Agricultural machines (AMs) refer to equipment usually used in agriculture such as tractors,
hand tools, and power tools. It reduces the labor work, increases farms produce, enhances …
hand tools, and power tools. It reduces the labor work, increases farms produce, enhances …
Using deep learning based approaches for bearing fault diagnosis with AE sensors
In the age of Internet of Things and Industrial 4.0, the prognostic and health management
(PHM) systems are used to collect massive real-time data from mechanical equipment …
(PHM) systems are used to collect massive real-time data from mechanical equipment …
Aircraft gearbox fault diagnosis system: an approach based on deep learning techniques
Gearbox is one of the vital components in aircraft engines. If any small damage to gearbox, it
can cause the breakdown of aircraft engine. Thus it is significant to study fault diagnosis in …
can cause the breakdown of aircraft engine. Thus it is significant to study fault diagnosis in …
Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review
A smart factory is a highly digitized and connected production facility that relies on smart
manufacturing. Additionally, artificial intelligence is the core technology of smart factories …
manufacturing. Additionally, artificial intelligence is the core technology of smart factories …
Machinery fault diagnosis based on a modified hybrid deep sparse autoencoder using a raw vibration time-series signal
Intelligent fault diagnosis (IFD) is an effective system to ensure the safe operation of
mechanical components such as bearings, gears, and blades. The main challenge of IFD …
mechanical components such as bearings, gears, and blades. The main challenge of IFD …
Deep learning and its applications to machine health monitoring
Abstract Since 2006, deep learning (DL) has become a rapidly growing research direction,
redefining state-of-the-art performances in a wide range of areas such as object recognition …
redefining state-of-the-art performances in a wide range of areas such as object recognition …
A stacked autoencoder‐based deep neural network for achieving gearbox fault diagnosis
G Liu, H Bao, B Han - Mathematical Problems in Engineering, 2018 - Wiley Online Library
Machinery fault diagnosis is pretty vital in modern manufacturing industry since an early
detection can avoid some dangerous situations. Among various diagnosis methods, data …
detection can avoid some dangerous situations. Among various diagnosis methods, data …
An intelligent deep feature learning method with improved activation functions for machine fault diagnosis
Rotating machinery has been developed with high complexity and precision, and bearings
and gears are crucial components in the machinery system. Deep learning has attracted …
and gears are crucial components in the machinery system. Deep learning has attracted …
Deep learning algorithms for machinery health prognostics using time-series data: A review
Background An intelligent predictive health management paradigm for industrial machinery
is inevitable in Industry 4.0. The machinery health failure/degradation data acquired as time …
is inevitable in Industry 4.0. The machinery health failure/degradation data acquired as time …