Multi-scale remaining useful life prediction using long short-term memory

Predictive maintenance based on performance degradation is a crucial way to reduce maintenance costs and potential failures in modern complex engineering systems. Reliable remaining useful life (RUL) prediction is the main criterion for decision-making in predictive maintenance. Conventional model-based methods and data-driven approaches often fail to achieve an accurate prediction result using a single model for a complex system featuring multiple components and operational conditions, as the degradation pattern is usually nonlinear and time-varying. This paper proposes a novel multi-scale RUL prediction approach adopting the Long Short-Term Memory (LSTM) neural network. In the feature engineering phase, Pearson’s correlation coefficient is applied to extract the representative features, and an operation-based data normalisation approach is presented to deal with the cases where multiple degradation patterns are concealed in the sensor data. Then, a three-stage RUL target function is proposed, which segments the degradation process of the system into the non-degradation stage, the transition stage, and the linear degradation stage. The classification of these three stages is regarded as the small-scale RUL prediction, and it is achieved through processing sensor signals after the feature engineering using a novel LSTM-based binary classification algorithm combined with a correlation method. After that, a specific LSTM-based predictive model is built for the last two stages to produce a large-scale RUL prediction. The proposed approach is validated by comparing it with several state-of-the-art techniques based on the widely used C-MAPSS dataset. A significant improvement is achieved in RUL prediction performance in most subsets. For instance, a 40% reduction is achieved in Root Mean Square Error over the best existing method in subset FD001. Another contribution of the multi-scale RUL prediction approach is that it offers more degree of flexibility of prediction in the maintenance strategy depending on data availability and which degradation stage the system is in

Practical options for adopting recurrent neural network and its variants on remaining useful life prediction

The remaining useful life (RUL) of a system is generally predicted by utilising the data collected from the sensors that continuously monitor different indicators. Recently, different deep learning (DL) techniques have been used for RUL prediction and achieved great success. Because the data is often time-sequential, recurrent neural network (RNN) has attracted significant interests due to its efficiency in dealing with such data. This paper systematically reviews RNN and its variants for RUL prediction, with a specific focus on understanding how different components (e.g., types of optimisers and activation functions) or parameters (e.g., sequence length, neuron quantities) affect their performance. After that, a case study using the well-studied NASA’s C-MAPSS dataset is presented to quantitatively evaluate the influence of various state-of-the-art RNN structures on the RUL prediction performance. The result suggests that the variant methods usually perform better than the original RNN, and among which, Bi-directional Long Short-Term Memory generally has the best performance in terms of stability, precision and accuracy. Certain model structures may fail to produce valid RUL prediction result due to the gradient vanishing or gradient exploring problem if the parameters are not chosen appropriately. It is concluded that parameter tuning is a crucial step to achieve optimal prediction performance

Recurrent neural networks and its variants in Remaining Useful Life prediction

Data-driven techniques, especially artificial intelligence (AI) based deep learning (DL) techniques, have attracted more and more attention in the manufacturing sector because of the rapid growth of the industrial Internet of Things (IoT) and Big Data. Tremendous researches of DL techniques have been applied in machine health monitoring, but still very limited works focus on the application of DL on the Remaining Useful Life (RUL) prediction. Precise RUL prediction can significantly improve the reliability and operational safety of industrial components or systems, avoid fatal breakdown and reduce the maintenance costs. This paper reviews and compares the state-of-the-art DL approaches for RUL prediction focusing on Recurrent Neural Networks (RNN) and its variants. It has been observed from the results for a publicly available dataset that Long Short-Term Memory (LSTM) networks and Gated Recurrent Unit (GRU) networks outperform the basic RNNs, and the number of the network layers affects the performance of the prediction

Three-stage feature selection approach for deep learning-based RUL prediction methods

The remaining useful life (RUL) prediction plays an increasingly important role in predictive maintenance. With the development of big data and the Internet-of-Things (IoT), deep learning (DL) techniques have been widely adopted for RUL prediction. Addressing the limitation of the current methods for data under multiple operating conditions, this paper proposes a three-stage feature selection approach for DL-based RUL prediction models. The k-medoids cluster is initially used to sort raw data based on different operating conditions. In the first stage of feature selection, an operational-based normalisation approach is applied to reconstruct the data. Afterwards, Spearman's rank and pair-wise Pearson correlation coefficients are used to eliminate irrelevant and redundant features in the second and third stages, respectively. A case study using NASA's Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset is presented to quantitatively evaluate the influence of the proposed feature selection method using the Recurrent Neural Network (RNN) and its’ variants, enhanced by an optimised activation function and optimiser. The results confirm that the proposed method can improve the stability of DL models and achieve about a 7.3% average improvement in the RUL prediction for popular and state-of-the-art DL models

New structure to design interval observers for linear continuous-time systems

International audienceIn this contribution, a new approach to design interval observer for continuous-time linear systems is introduced. First, a more general structure than the classical Luenberger observer is proposed and extended to the interval case based on an explicit set integration method. Then, to obtain tight state enclosures, the computation of the design matrices involved in this new structure is formulated as a nonlinear constrained optimisation problem. In addition, using the symmetric property of interval vectors, a less complex form of the proposed interval observer is presented. On the other hand, compared to the conventional interval observers, no preserving order property on the dynamics of the estimation error is required to apply the introduced method. A numerical case study example is considered to illustrate the effectiveness of the proposed method and to compare its performance to that of a former method

Fault diagnosis by interval‐based adaptive thresholds and peak‐to‐peak observers

International audienceThis article investigates actuator fault diagnosis for continuous-time systems with unknown but bounded uncertainties. A novel interval-based adaptive threshold computation approach is proposed for residual evaluation. Meanwhile, peak-to-peak performance is applied to generate robust residuals against system uncertainties. By integrating the designed peak-to-peak residual generator with adaptive thresholds, we can achieve promising fault detection results. Furthermore, based on the general observer scheme, the proposed residual generator and adaptive thresholds can be equally used for fault isolation. Simulation results are given to illustrate the effectiveness and superiority of the proposed fault detection and isolation method

An Improved Zonotopic Approach Applied to Fault Detection for Takagi-Sugeno Fuzzy Systems

International audiencen this work, an actuator fault detection problem for discrete-time Takagi-Sugeno fuzzy systems is tackled in a bounded error context where both state disturbances and measurement noise are assumed to be unknown but bounded with known bounds. First, a peak-to-peak performance synthesis method is applied to design a robust residual generator against the considered process disturbances and measurement noise. Meanwhile, an improved zonotopic approach is proposed to compute tight adaptive thresholds for residual evaluation. Then, a reliable set-membership fault detection strategy with the aid of generated residual signals and adaptive thresholds is introduced. Finally, the viability of the proposed method is demonstrated via a numerical simulation. Then, an experimentation on a 3D Crane system is performed to show its practicability

SnS homojunction nanowire-based solar cells

National Science Foundation of China [50902117, 50825101]; Natural Science Foundation of Fujian Province of China [2009J01263]Doped p-n homojunction single-crystalline SnS nanowire arrays were synthesized on an aluminum foil substrate using Au nanoparticles as the catalyst. These nanowires were fabricated into photovoltaic cells and showed a defined rectifying behavior in darkness. Under AM1.5G illumination at 100 mW cm(-2), the cell had a high short-circuit photocurrent density of 7.64 mA cm(-2) and energy conversion efficiency of 1.95%. This study provides an experimental demonstration for integrating one-dimensional nanostructure arrays with the substrate to fabricate homojunction photovoltaic cells directly

C-Sprinkler [smart irrigation system]

The C-Sprinkler is a smart irrigation system which has independent AI, is remote controlled and cloud service.  It has the original functions of tradition irrigation system, such as timers and changing directions of the sprinkler. Additionally, it can automatically irrigate the garden and help users to free their hands and still maintain the garden well, even if they are away from their home.&nbsp

Structure and optical properties of SnS nanowire arrays prepared with two-step method

Single crystalline SnS nanowire arrays have been synthesized by sulfurating the Sn nanowire arrays which were prepared with the electrochemical deposition. The obtained SnS nanowire arrays are charactered with the XRD, SEM, TEM and the UV/Visible/NIR spectrophotometer. And the results indicate that the nanowires with an average diameter of 50 nm and a length of several tens micrometers, which same with the as prepared Sn nanowires. There are two absorption peaks indicate with the direct and indirect bandgaps about the orthorhombic SnS nanowire arrays