An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring

By Zhigang Tian. Journal of Intelligent Manufacturing, 2009

374 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/bfab5cc3ccf363957f6d160644c1e3b2cc4e98a9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Xiang Li
2. Qian Ding
3. Yaguo Lei
4. Naipeng Li
5. Ruqiang Yan
6. Jing Lin
7. Cheng-Geng Huang
8. Hongzhong Huang

In Mechanical Engineering

1. Xiang Li
2. Qian Ding
3. Cheng-Geng Huang
4. Hongzhong Huang
5. Yanfeng Li
6. Ruqiang Yan
7. Naipeng Li
8. Yaguo Lei

In Mechanics of Materials

1. Yaguo Lei
2. Naipeng Li
3. Sankaran Mahadevan
4. Zhen Hu
5. Jing Lin
6. Chao Hu
7. Xiang Li
8. Cheng-Geng Huang

Citations in the corpus

• A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions (2019, background): “The former one adopts structure VI as described in Section IV-D1; the latter utilizes four hidden layers with 32 neurons in the first BLSTM layer, 32 neurons in the second BLSTM layer, 8 and 8 neurons in the third and the fourth layer where FFNN was used, and 1 neuron in the final output layer for p”
• Cloud-enabled prognosis for manufacturing (2015, methodology): “Age and multiple condition monitoring measurements were fitted for a failure history through a generalized Weibull-FR function by Tian [75], who then used the fitted measurement value as the ANN input.”
• Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction (2014, neutral): “There have been more interests lately on various types of neural networks and neural-fuzzy systems [28–38].”
• Long Short-Term Memory Network for Remaining Useful Life estimation (2017, background): “There are time dependency between sliding windows, but sliding windows considered are independent in these traditional approaches [2], [3].”
• A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, methodology): “Direct mapping approaches bypass health forecasting and directly map features (ML model input) to RUL (ML model output) using ML models such as standard feedforward neural networks (Huang et al. 2007; Tian 2012), deep CNN (Li et al.”
• Remaining useful life estimation in prognostics using deep convolution neural networks (2018, methodology): “Tian [17] developed an artificial neural network (ANN) method for estimating the RUL of equipment.”
• Remaining Useful Lifetime Prediction via Deep Domain Adaptation (2019, neutral): “In particular, authors have proposed several methods that attempt to extract the relationships of acquired sequential data and RUL prediction, such as linear regression [23], Support Vector Regression (SVR) [8], fuzzy-logic systems [24] and neural networks [25].”
• An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings (2015, background): “Therefore, the RUL prediction of rolling element bearings has attracted increasingly more attention in recent years [1]–[4].”
• Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach (2021, methodology): “algorithms, such as artificial neural network [12], [13], extreme learning machine (ELM) [14], SVM [15], [16], neural”