Vibration Analysis of Heterogeneous Gearbox Faults using EMD Features and SVM Classifier

Gearbox is one of the important mechanical power transmission device most commonly used in automobiles and industries to get the desired change in speed and torque. The gearbox fault diagnosis has given utmost importance for its significance in preventing halts of a mechanical system and guaranteeing an advantage of sufficient maintenance. This paper presents the vibration analysis of heterogeneous gearbox faults using EMD features and SVM classifier. The vibration signal is converted into intrinsic mode functions (IMF) with decreasing order of frequencies using empirical mode decomposition (EMD) method. Feature vector consisting of information theoretic features have been computed for each IMF and concatenated to form a feature set. By using random permutations, the feature set has been divided into training and testing sets. The support vector machine (SVM) algorithm has been used as a classification technique to diagnose the gearbox faults, which consists of five-class classification. The accuracy of the developed algorithm has been validated using 100 Monte Carlo runs. A comparative study has been carried between computed features and varying IMF components. The observations made were - clear discrimination of the gearbox faults and improved classification accuracy, which contain - chipped tooth, missing tooth, root fault, surface fault and healthy working state of the gear

Statistical analysis of turbo generator sets failure causes

Failure diagnostics and general decrease of accident rate at power plants is a major task of energy generation industry, and solution of it provides reliable energy supply country wide and technological progress in mechanical engineering. Along with some other crucial means, the task could be solved by means of teaching the maintenance staff based on accidents that have already occurred. That is no secret that everywhere in the world due to indecision or misinterpretation a huge number of accidents have happened merely because the personnel were not aware of similar cases at other power plants. Nevertheless with the development of computational technologies and mathematical algorithms the role of personnel in some cases has been reduced to observation and action in critical situation, while the rest is performed by machines: various types of diagnostics and prediction of failure systems based on artificial neural networks are widely applied and developed. However, in order to train these systems, it is absolutely required to know the reasons that could lead to and consequences that could follow some deterioration in turbo generator sets performance. The aim of the present paper is to give statistical analysis of turbo generator sets failure reasons based on open source data presented by Russian and foreign researchers and analysts in the field. The statistical data could be used to perform classification and ranking of failure reasons in terms of frequency of occurrence, possibility to identify or detect, etc. and the paper also gives brief listing of possible ways of detection or identification of failure modes and possible consequences for the main units of a turbo generator

Artificial Intelligence in Supply Chain Management: Investigation of Transfer Learning to Improve Demand Forecasting of Intermittent Time Series with Deep Learning

Demand forecasting intermittent time series is a challenging business problem. Companies have difficulties in forecasting this particular form of demand pattern. On the one hand, it is characterized by many non-demand periods and therefore classical statistical forecasting algorithms, such as ARIMA, only work to a limited extent. On the other hand, companies often cannot meet the requirements for good forecasting models, such as providing sufficient training data. The recent major advances of artificial intelligence in applications are largely based on transfer learning. In this paper, we investigate whether this method, originating from computer vision, can improve the forecasting quality of intermittent demand time series using deep learning models. Our empirical results show that, in total, transfer learning can reduce the mean square error by 65 percent. We also show that especially short (65 percent reduction) and medium long (91 percent reduction) time series benefit from this approach

Imitation model of unbalanced rotor on fluid-film bearings

Diagnostics of rotating machines is taking a new step forward with the development of intellectual technologies based on predictive and machine learning tools. Despite having a range of advantages compared to humans in terms of big data processing powers, correlation and various feature extraction possibilities, and swiftness of operation, such systems are limited by measurement system elements in terms of their parameters: sensors and ADCs with their sensitivity properties and accuracy restrictions, microprocessors with limitation of processing powers, etc. Experimental data is used to recreate experimental environment in simulation of induced unbalance. The imitation model is based on rotor dynamics equations of rotor motion, Reynolds equation to estimate reaction forces of a fluid-film bearing and takes into account sensor parameters and position, gear coupling effect and other measurement system elements parameters. The results show that under certain conditions it becomes impossible to successfully track unbalance which in real conditions could lead to malfunction or failure of a rotor machine

Short-Term Wind Speed and Temperature Forecasting Model Based on Gated Recurrent Unit Neural Networks

Wind energy generation fluctuations and intermittency issues create inefficiency and instability in power management. The recurrent neural networks (RNNs) prediction approaches are an essential technology that can improve wind power generation and assist in energy management and power systems’ performance. In this paper, a prediction model based on Gated Recurrent Unit (GRU) neural networks is proposed to predict wind speed and temperature values one week ahead in the future at hourly intervals. The GRU prediction model automatically learnt the features, used fewer training parameters, and required a shorter time to train compared to other types of RNNs. The GRU model was designed to predict 169 hours ahead as a short-term period of wind speed and temperature values based on 36 years of hourly historical data (1 January 1985 to 6 June 2021) collected from Dumat al-Jandal city. The findings notably indicate that the GRU model has promising performance with significant prediction accuracies in terms of overfitting, reliability, resolution, efficiency, and generalizable processes. The GRU model is characterized by its good performance and influential evaluation error metrics for wind speed and temperature values

Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning

This paper focuses on bearing fault diagnosis with limited training data. A major challenge in fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type under all working conditions. Recently deep learning based fault diagnosis methods have achieved promising results. However, most of these methods require large amount of training data. In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data. Our model is based on the siamese neural network, which learns by exploiting sample pairs of the same or different categories. Experimental results over the standard Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset showed that our few-shot learning approach is more effective in fault diagnosis with limited data availability. When tested over different noise environments with minimal amount of training data, the performance of our few-shot learning model surpasses the one of the baseline with reasonable noise level. When evaluated over test sets with new fault types or new working conditions, few-shot models work better than the baseline trained with all fault types. All our models and datasets in this study are open sourced and can be downloaded from https://mekhub.cn/as/fault_diagnosis_with_few-shot_learning/

Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction

Deep learning (DL) based predictive models from electronic health records (EHR) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required to achieve high accuracy, hindering the adoption of DL-based models in scenarios with limited training data size. Recently, bidirectional encoder representations from transformers (BERT) and related models have achieved tremendous successes in the natural language processing domain. The pre-training of BERT on a very large training corpus generates contextualized embeddings that can boost the performance of models trained on smaller datasets. We propose Med-BERT, which adapts the BERT framework for pre-training contextualized embedding models on structured diagnosis data from 28,490,650 patients EHR dataset. Fine-tuning experiments are conducted on two disease-prediction tasks: (1) prediction of heart failure in patients with diabetes and (2) prediction of pancreatic cancer from two clinical databases. Med-BERT substantially improves prediction accuracy, boosting the area under receiver operating characteristics curve (AUC) by 2.02-7.12%. In particular, pre-trained Med-BERT substantially improves the performance of tasks with very small fine-tuning training sets (300-500 samples) boosting the AUC by more than 20% or equivalent to the AUC of 10 times larger training set. We believe that Med-BERT will benefit disease-prediction studies with small local training datasets, reduce data collection expenses, and accelerate the pace of artificial intelligence aided healthcare.Comment: L.R., X.Y., and Z.X. share first authorship of this wor

Eigen-spectrograms: an interpretable feature space for bearing fault diagnosis based on artificial intelligence and image processing

The Intelligent Fault Diagnosis of rotating machinery proposes some captivating challenges in light of the imminent big data era. Although results achieved by artificial intelligence and deep learning constantly improve, this field is characterized by several open issues. Models' interpretation is still buried under the foundations of data driven science, thus requiring attention to the development of new opportunities also for machine learning theories. This study proposes a machine learning diagnosis model, based on intelligent spectrogram recognition, via image processing. The approach is characterized by the introduction of the eigen-spectrograms and randomized linear algebra in fault diagnosis. The eigen-spectrograms hierarchically display inherent structures underlying spectrogram images. Also, different combinations of eigen-spectrograms are expected to describe multiple machine health states. Randomized algebra and eigen-spectrograms enable the construction of a significant feature space, which nonetheless emerges as a viable device to explore models' interpretations. The computational efficiency of randomized approaches further collocates this methodology in the big data perspective and provides new reading keys of well-established statistical learning theories, such as the Support Vector Machine (SVM). The conjunction of randomized algebra and Support Vector Machine for spectrogram recognition shows to be extremely accurate and efficient as compared to state of the art results.Comment: 14 pages, 13 figure