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

Opportunistic Preventive Maintenance Scheduling Based on Theory of Constraints

Abstract In recent years, to improve system reliability and economy, machine maintenance strategies have been paid more and more intention by researchers. This paper aims to integrate the concept of theory of constraints (TOC) into multi-machine opportunistic maintenance policy. Based on the preventive maintenance algorithm, an improved model containing bottleneck strategy which influences opportunistic maintenance has been developed. By maximizing total cost saving, an optimal maintenance schedule of all machines in a series product line can be obtained. The results of a case study show that this model is valid for planning a comprehensive optimal maintenance schedule. Furthermore, by comparing with other models, this model has been proven to be more effectively especially in series product lines with bottleneck

A Novel Health Prognosis Method for a Power System Based on a High-Order Hidden Semi-Markov Model

Power system health prognosis is a key process of condition-based maintenance. For the problem of large error in the residual lifetime prognosis of a power system, a novel residual lifetime prognosis model based on a high-order hidden semi-Markov model (HOHSMM) is proposed. First, HOHSMM is developed based on the hidden semi-Markov model (HSMM). An order reduction method and a composite node mechanism of HOHSMM based on permutation are proposed. The health state transition matrix and observation matrix are improved accordingly. The high-order model is transformed into the corresponding first-order model, and more node dependency information is stored in the parameter group to be estimated. Secondly, in order to estimate the parameters and optimize the structure of the proposed model, an intelligent optimization algorithm group is used instead of the expectation–maximization (EM) algorithm. Thus, the simplification of the topology of the high-order model by the intelligent optimization algorithm can be realized. Then, the state duration variables in the high-order model are defined and deduced. The prognosis method based on polynomial fitting is used to predict the residual lifetime of the power system when the prior distribution is unknown. Finally, the intelligent optimization algorithm is used to solve the proposed model, and experiments are performed based on a set of power system data sets to evaluate the performance of the proposed model. Compared with HSMM, the proposed model has better performance on the power system health prognosis problem and can get a relatively good solution in a short computation time

Optimal selective maintenance scheduling for series–parallel systems based on energy efficiency optimization

With the industry sustainable development and increasing awareness of energy conservation, many manufacturing enterprises prefer to develop the operation and maintenance (O&M) with high production throughput and low energy consumption. In reality, many manufacturing systems are required to conduct a sequence of predefined missions with finite breaks between any two consecutive missions. To successfully complete the next mission production, maintenance actions are arranged and performed on machines during each scheduled break. In this paper, an energy-oriented selective maintenance policy (ESMP) for series–parallel systems is investigated. At each break, multiple maintenance actions with different impacts on machine degradation are available under limited maintenance resources. To obtain the throughput-and-energy based maintenance scheme, we first model the system energy efficiency based on system throughput and energy consumption. Then, we integer the energy efficiency modeling, production throughout analysis, and selective maintenance scheduling into an optimization model. And the model objective is to find the appropriate maintenance action for each machine at each break subject to cost and duration constraints. Numerical examples have been addressed to demonstrate the performance and adaptability of our proposed ESMP in long-term selective maintenance scheduling. Finally, a comparative analysis with traditional reliability-oriented policy shows the significant improvement of energy efficiency

A Deep Learning Feature Fusion Based Health Index Construction Method for Prognostics Using Multiobjective Optimization

International audienceDegradation modeling and prognostics serve as the basis for system health management. Recently, various sensors provide plentiful monitoring data that can reflect the system status. A multitude of feature fusion techniques based on multisensor data have been proposed to generate a composite health index (HI) for prognostics, which can represent the underlying degradation mechanism. Most existing methods have used linear fusion models and neglected the practical requirements for HI construction, which are insufficient to reveal the nonlinear relations among features and difficult to obtain accurate HIs for complicated systems. This study proposes a novel feature fusion-based HI construction method with deep learning and multiobjective optimization. Multiple degradation features are fused by a deep neutral network (DNN). Several desired properties that the HIs should have for prognostics are adopted to formulate the objective functions of DNN training. To balance the spatial complexity and performance of the fusion model, a multiobjective optimization model is generated for training the DNN. Then, a generalized nonlinear Wiener process model is used to predict the remaining useful life with the resulted HIs. Finally, two cases are analyzed to illustrate the effectiveness and robustness of the proposed method

Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines

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A New Support Vector Regression Model for Equipment Health Diagnosis with Small Sample Data Missing and Its Application

Actually, it is difficult to obtain a large number of sample data due to equipment failure, and small sample data may also be missing. This paper proposes a novel small sample data missing filling method based on support vector regression (SVR) and genetic algorithm (GA) to improve equipment health diagnosis effect. First, the genetic algorithm is used to optimize support vector regression, and a new method GA-SVR can be proposed. The GA-SVR model is trained by using other data of the variable to which the missing data belongs, and the single-variable prediction method can be obtained. The correlation analysis is used to reconstruct the training set, and the GA-SVR is trained by using the data of the variables related to the missing data to obtain the multivariate prediction method. Then, the dynamic weight is presented to combine the single-variable prediction method with the multiple-variable prediction method based on certain principles, and the missing data are filled with the combined prediction methods. The filled data are used as input of GA-SVM to diagnose equipment failure. Finally, a case study is given to verify the applicability and effectiveness of the proposed method