Incipient Fault Diagnosis of Batch Process Based on Deep Time Series Feature Extraction

The prediction model for haze pollution based on stacking framework and feature extraction of time series images

In this paper, we propose a new model called "image-feature-stacking prediction model" to study the prediction problem of univariate time series data. Its main idea is to convert univariate time series data into corresponding images, and then use the optimized Inception-v1 network to extract hidden features from the images as input variables, based on these features, a two-layer stacking ensemble learning framework is constructed to output the final predicted values. The main contribution of the newly proposed model is to convert one-dimensional time series data into two-dimensional images, and automatically extract features from images. This method can truly mine the intrinsic relationship between the data instead of simply relying on descriptive statistical features to replace the original time series, thereby improving the prediction performance of the model. We use the new prediction model to predict daily PM_2.5 concentration, for one-step prediction, the results show that compared with the other three time series prediction models, the proposed prediction model reduces the mean absolute percentage error and mean absolute scaled error to 19.204% and 1.242, respectively, which is 76.607% and 77.004% lower than the maximum value of mean absolute percentage error and mean absolute scaled error of four prediction models. We also make two-step and three-step predictions, and the newly proposed model also shows encouraging performance.

An Improved Incipient Fault Diagnosis Method of Bearing Damage Based on Hierarchical Multi-Scale Reverse Dispersion Entropy

The amplitudes of incipient fault signals are similar to health state signals, which increases the difficulty of incipient fault diagnosis. Multi-scale reverse dispersion entropy (MRDE) only considers difference information with low frequency range, which omits relatively obvious fault features with a higher frequency band. It decreases recognition accuracy. To defeat the shortcoming with MRDE and extract the obvious fault features of incipient faults simultaneously, an improved entropy named hierarchical multi-scale reverse dispersion entropy (HMRDE) is proposed to treat incipient fault data. Firstly, the signal is decomposed hierarchically by using the filter smoothing operator and average backward difference operator to obtain hierarchical nodes. The smoothing operator calculates the mean sample value and the average backward difference operator calculates the average deviation of sample values. The more layers, the higher the utilization rate of filter smoothing operator and average backward difference operator. Hierarchical nodes are obtained by these operators, and they can reflect the difference features in different frequency domains. Then, this difference feature is reflected with MRDE values of some hierarchical nodes more obviously. Finally, a variety of classifiers are selected to test the separability of incipient fault signals treated with HMRDE. Furthermore, the recognition accuracy of these classifiers illustrates that HMRDE can effectively deal with the problem that incipient fault signals cannot be easily recognized due to a similar amplitude dynamic.