A Temporal Pyramid Pooling-Based Convolutional Neural Network for Remaining Useful Life Prediction

Remaining Useful Life (RUL) prediction is a key issue in Prognostics and Health Management (PHM). Accurate RUL assessments are crucial for predictive maintenance planning. Deep neural networks such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) have been widely applied in RUL prediction due to their powerful feature learning capabilities in dealing with high-dimensional sensor data. The sliding time window method with a predefined window size is typically employed to generate data samples to train such deep neural networks. However, the disadvantage of using a fixed-size time window is that we might not be able to apply the resulting predictive model to predict new sensor data whose length is shorter than the predetermined time window size. Besides, as the length of sensor data varies, the traditional unchanged and subjectively set time window size may be inappropriate and impair the prediction model’s performance. Therefore, we propose a Temporal Pyramid Pooling-Based Convolutional Neural Network (TPP-CNN) to increase model practicability and prediction accuracy. With the temporal pyramid pooling module, we can generate data samples of arbitrary time window sizes and use them as inputs of CNN. In the training phase, CNN can learn to capture temporal dependencies of different lengths since we feed in samples with different time window sizes. In this novel manner, the learned model can be used to test data with arbitrary sizes, and its predictive ability is also improved. The proposed TPP-CNN model is validated on the C-MPASS turbofan engine dataset, and the experiments have demonstrated its effectiveness

On the potential of time delay neural networks to detect indirect coupling between time series

Determining the coupling between systems remains a topic of active research in the field of complex science. Identifying the proper causal influences in time series can already be very challenging in the trivariate case, particularly when the interactions are non-linear. In this paper, the coupling between three Lorenz systems is investigated with the help of specifically designed artificial neural networks, called time delay neural networks (TDNNs). TDNNs can learn from their previous inputs and are therefore well suited to extract the causal relationship between time series. The performances of the TDNNs tested have always been very positive, showing an excellent capability to identify the correct causal relationships in absence of significant noise. The first tests on the time localization of the mutual influences and the effects of Gaussian noise have also provided very encouraging results. Even if further assessments are necessary, the networks of the proposed architecture have the potential to be a good complement to the other techniques available in the market for the investigation of mutual influences between time serie

Evolutionary multivariate time series prediction

Multivariate time series (MTS) prediction plays a significant role in many practical data mining applications, such as finance, energy supply, and medical care domains. Over the years, various prediction models have been developed to obtain robust and accurate prediction. However, this is not an easy task by considering a variety of key challenges. First, not all channels (each channel represents one time series) are informative (channel selection). Considering the complexity of each selected time series, it is difficult to predefine a time window used for inputs. Second, since the selected time series may come from cross domains collected with different devices, they may require different feature extraction techniques by considering suitable parameters to extract meaningful features (feature extraction), which influences the selection and configuration of the predictor, i.e., prediction (configuration). The challenge arising from channel selection, feature extraction, and prediction (configuration) is to perform them jointly to improve prediction performance. Third, we resort to ensemble learning to solve the MTS prediction problem composed of the previously mentioned operations,  where the challenge is to obtain a set of models satisfied both accurate and diversity. Each of these challenges leads to an NP-hard combinatorial optimization problem, which is impossible to be solved using the traditional methods since it is non-differentiable. Evolutionary algorithm (EA), as an efficient metaheuristic stochastic search technique, which is highly competent to solve complex combinatorial optimization problems having mixed types of decision variables, may provide an effective way to address the challenges arising from MTS prediction. The main contributions are supported by the following investigations. First, we propose a discrete evolutionary model, which mainly focuses on seeking the influential subset of channels of MTS and the optimal time windows for each of the selected channels for the MTS prediction task. A comprehensively experimental study on a real-world electricity consumption data with auxiliary environmental factors demonstrates the efficiency and effectiveness of the proposed method in searching for the informative time series and respective time windows and parameters in a predictor in comparison to the result obtained through enumeration. Subsequently, we define the basic MTS prediction pipeline containing channel selection, feature extraction, and prediction (configuration). To perform these key operations, we propose an evolutionary model construction (EMC) framework to seek the optimal subset of channels of MTS, suitable feature extraction methods and respective time windows applied to the selected channels, and parameter settings in the predictor simultaneously for the best prediction performance. To implement EMC, a two-step EA is proposed, where the first step EA mainly focuses on channel selection while in the second step, a specially designed EA works on feature extraction and prediction (configuration). A real-world electricity data with exogenous environmental information is used and the whole dataset is split into another two datasets according to holiday and nonholiday events. The performance of EMC is demonstrated on all three datasets in comparison to hybrid models and some existing methods. Then, based on the prediction pipeline defined previously, we propose an evolutionary multi-objective ensemble learning model (EMOEL) by employing multi-objective evolutionary algorithm (MOEA) subjected to two conflicting objectives, i.e., accuracy and model diversity. MOEA leads to a pareto front (PF) composed of non-dominated optimal solutions, where each of them represents the optimal subset of the selected channels, the selected feature extraction methods and the selected time windows, and the selected parameters in the predictor. To boost ultimate prediction accuracy, the models with respect to these optimal solutions are linearly combined with combination coefficients being optimized via a single-objective task-oriented EA. The superiority of EMOEL is identified on electricity consumption data with climate information in comparison to several state-of-the-art models. We also propose a multi-resolution selective ensemble learning model, where multiple resolutions are constructed from the minimal granularity using statistics. At the current time stamp, the preceding time series data is sampled at different time intervals (i.e., resolutions) to constitute the time windows. For each resolution, multiple base learners with different parameters are first trained. Feature selection technique is applied to search for the optimal set of trained base learners and least square regression is used to combine them. The performance of the proposed ensemble model is verified on the electricity consumption data for the next-step and next-day prediction. Finally, based on EMOEL and multi-resolution, instead of only combining the models generated from each PF, we propose an evolutionary ensemble learning (EEL) framework, where multiple PFs are aggregated to produce a composite PF (CPF) after removing the same solutions in PFs and being sorted into different levels of non-dominated fronts (NDFs). Feature selection techniques are applied to exploit the optimal subset of models in level-accumulated NDF and least square is used to combine the selected models. The performance of EEL that chooses three different predictors as base learners is evaluated by the comprehensive analysis of the parameter sensitivity. The superiority of EEL is demonstrated in comparison to the best result from single-objective EA and the best individual from the PF, and several state-of-the-art models across electricity consumption and air quality datasets, both of which use the environmental factors from other domains as the auxiliary factors. In summary, this thesis provides studies on how to build efficient and effective models for MTS prediction. The built frameworks investigate the influential factors, consider the pipeline composed of channel selection, feature extraction, and prediction (configuration) simultaneously, and keep good generalization and accuracy across different applications. The proposed algorithms to implement the frameworks use techniques from evolutionary computation (single-objective EA and MOEA), machine learning and data mining areas. We believe that this research provides a significant step towards constructing robust and accurate models for solving MTS prediction problems. In addition, with the case study on electricity consumption prediction, it will contribute to helping decision-makers in determining the trend of future energy consumption for scheduling and planning of the operations of the energy supply system