scholarly journals Remaining Useful Life Prediction of Quay Crane Hoist Gearbox Bearing under Dynamic Operating Conditions Based on ARIMA-CAPF Framework

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhiwei Sun ◽  
Xiong Hu ◽  
Kai Dong
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Production Safety ◽  
State Transfer ◽  
Bearing Life ◽  
Degradation Rates ◽  
Dynamic Operating ◽  
Useful Life

The remaining useful life (RUL) prediction of quay crane (QC) bearings is of great significance to port production safety. An RUL prediction framework of QC bearing under dynamic conditions is proposed. Firstly, the load is discretized, and the corresponding operating conditions are classified. Then, the Autoregressive Integrated Moving Average (ARIMA) model is utilized to predict the load and corresponding operating conditions. Secondly, a Wiener process considering degradation rates and jump coefficients under different operating conditions is developed as the state transfer function. Finally, a condition-activated particle filter (CAPF) is proposed to predict the system state and the bearing’s RUL. The proposed prediction framework is verified by the hoist bearing life cycle data from a port in Shanghai collected by the NetCMAS system. The prediction results by the ARIMA-CAPF framework in comparison with three other prediction strategies identify the effectiveness.

scholarly journals Remaining useful life prediction of PEMFC systems under dynamic operating conditions

2021 ◽  
Vol 231 ◽  
pp. 113825
Author(s):  
Zhiguang Hua ◽  
Zhixue Zheng ◽  
Elodie Pahon ◽  
Marie-Cécile Péra ◽  
Fei Gao
Keyword(s):  
Life Prediction ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Dynamic Operating ◽  
Useful Life

Prognostics and RUL Estimations of SAC305, SAC105 and SnAg Solders Under Temperature and Vibration Using Long Short-Term Memory (LSTM) Deep Learning

2021 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Ken Blecker
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Short Term ◽  
Term Memory ◽  
Rectangular Pattern ◽  
Useful Life ◽  
Long Short Term Memory

Abstract Prognostics and Remaining Useful Life (RUL) estimations of complex systems are essential to operational safety, increased efficiency, and help to schedule maintenance proactively. Modeling the remaining useful life of a system with many complexities is possible with the rapid development in the field of deep learning as a computational technique for failure prediction. Deep learning can adapt to multivariate parameters complex and nonlinear behavior, which is difficult using traditional time-series models for forecasting and prediction purposes. In this paper, a deep learning approach based on Long Short-Term Memory (LSTM) network is used to predict the remaining useful life of the PCB at different conditions of temperature and vibration. This technique can identify the different underlying patterns in the time series that can predict the RUL. This study involves feature vector identification and RUL estimations for SAC305, SAC105, and Tin Lead solder PCBs under different vibration levels and temperature conditions. The acceleration levels of vibration are fixed at 5g and 10g, while the temperature levels are 55°C and 100°C. The test board is a multilayer FR4 configuration with JEDEC standard dimensions consists of twelve packages arranged in a rectangular pattern. Strain signals are acquired from the backside of the PCB at symmetric locations to identify the failure of all the packages during vibration. The strain signals are resistance values that are acquired simultaneously during the experiment until the failure of most of the packages on the board. The feature vectors are identified from statistical analysis on the strain signals frequency and instantaneous frequency components. The principal component analysis is used as a data reduction technique to identify the different patterns produced from the four strain signals with failures of the packages during vibration. LSTM deep learning method is used to model the RUL of the packages at different individual operating conditions of vibration for all three solder materials involved in this study. A combined model for RUL prediction for a material that can take care of the changes in the operating conditions is also modeled for each material.

scholarly journals Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation

2018 ◽  
Vol 8 (12) ◽  
pp. 2416 ◽  
Author(s):  
Ansi Zhang ◽  
Honglei Wang ◽  
Shaobo Li ◽  
Yuxin Cui ◽  
Zhonghao Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Recurrent Neural Networks ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Training Data ◽  
Good Prediction ◽  
Useful Life

Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results show that transfer learning can in general improve the prediction models on the dataset with a small number of samples. There is one exception that when transferring from multi-type operating conditions to single operating conditions, transfer learning led to a worse result.

scholarly journals Deep learning-based anomaly-onset aware remaining useful life estimation of bearings

2021 ◽  
Vol 7 ◽  
pp. e795
Author(s):  
Pooja Vinayak Kamat ◽  
Rekha Sugandhi ◽  
Satish Kumar
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Short Term Memory ◽  
Rotating Machinery ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Learning Models ◽  
Vibration Data ◽  
Degradation Data ◽  
Useful Life

Remaining Useful Life (RUL) estimation of rotating machinery based on their degradation data is vital for machine supervisors. Deep learning models are effective and popular methods for forecasting when rotating machinery such as bearings may malfunction and ultimately break down. During healthy functioning of the machinery, however, RUL is ill-defined. To address this issue, this study recommends using anomaly monitoring during both RUL estimator training and operation. Essential time-domain data is extracted from the raw bearing vibration data, and deep learning models are used to detect the onset of the anomaly. This further acts as a trigger for data-driven RUL estimation. The study employs an unsupervised clustering approach for anomaly trend analysis and a semi-supervised method for anomaly detection and RUL estimation. The novel combined deep learning-based anomaly-onset aware RUL estimation framework showed enhanced results on the benchmarked PRONOSTIA bearings dataset under non-varying operating conditions. The framework consisting of Autoencoder and Long Short Term Memory variants achieved an accuracy of over 90% in anomaly detection and RUL prediction. In the future, the framework can be deployed under varying operational situations using the transfer learning approach.

Fitness-for-Service and Remaining Useful Life Assessment of a Steam Drum

2003 ◽  
Author(s):  
Raymond K. Yee
Keyword(s):  
Power Plant ◽  
Small Sample ◽  
Analysis Procedure ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Life Assessment ◽  
Life Evaluation ◽  
Useful Life ◽  
Size Evaluation ◽  
Steam Drum

A steam drum in a typical power plant has experienced in-service cracking. Nondestructive examinations (NDE) were performed and a small sample was collected from the drum to evaluate the extent of the cracking that had occurred in the drum shell. Fitness-for-service and remaining useful life analyses of the drum were performed based on the NDE results and operating conditions. In this paper, the fitness-for-service analyses of the steam drum are described. The analysis procedure, material property determination, stress analysis, limiting flaw size evaluation, and remaining useful life evaluation for the drum are discussed. Recommendations for appropriate action are also presented.

scholarly journals A New Data-Driven Approach for Power IGBT Remaining Useful Life Estimation Based On Feature Reduction Technique and Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1571
Author(s):  
Adla Ismail ◽  
Lotfi Saidi ◽  
Mounir Sayadi ◽  
Mohamed Benbouzid
Keyword(s):  
Neural Network ◽  
Accelerated Aging ◽  
Principal Component ◽  
Life Time ◽  
Time Domain Analysis ◽  
Energy System ◽  
Remaining Useful Life ◽  
Feature Reduction ◽  
Operating Conditions ◽  
Useful Life

The insulated gate bipolar transistor (IGBT) is a crucial component of power converters (PCVs) and is commonly used in several PCVs topologies. On the other hand, the investigation and the study of the IGBT component show several changes within its behavior and lifetime, while this component is highly influenced by the operating conditions. Indeed, the monitoring of this component is necessary to minimize unexpected downtime of the wind energy system (WES). However, an accurate prediction of IGBTs remaining useful life (RUL) is the key enabler for life-time-optimized operation. Consequently, this work proposes a new prognostic approach for online IGBTs monitoring that adopts the time-domain analysis to extract useful information that is used as an input in the generation of the health indicator. Moreover, this approach is based on combining both of principal component analysis (PCA) technique and the feedforward neural network (FFNN) technique. PCA is used to reduce features extracted from IGBTs and the FFNN is implemented to achieve online regression of the trend parameter obtained from the PCA technique. To investigate and evaluate the performance of our idea we used the NASA Ames Laboratory Prognostics Center of Excellence IGBTs accelerated aging database. Finally, the achieved results clearly show the strength of the new trend parameter for IGBTs RUL prediction. The most notable strong correlation within the proposed approach is in relation to accuracy value, with an acceptable average accuracy rate of 60.4%.

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