Deep Generation Techniques in Task-Oriented Dialogue Systems

Building a task-oriented dialogue system like a restaurant booking is meaningful since it can largely reduce the working load of human and serve multiple users at the same time. A task-oriented dialogue system is often composed of a few modules, such as natural language understanding, dialogue state tracking, knowledgebase (KB) query, dialogue policy engine and response generation. Language understanding aims to convert the input to some predefined semantic frame. State tracking models explicitly the input semantic frame and the dialogue history for producing KB queries. Dialogue policy model decides on the system action which is then realized by a natural language generation component. The natural language generation component, particularly style-variation text generation, aims to map the meaning representations (MRs) and style (such as personality), we call them together as themes, to one or more corresponding natural language (NL) texts. A novel Focal-Variation Network (FVN) that learns latent distributions that closely follow the given themes are proposed for diverse text generation. Besides the language generation module, the other modules can also adopt the deep generation technique to achieve better performance: (1) multi-act generation in the policy engine module, (2) a flexible-structured end-to-end dialogue system based on a two-stage-decoder network. A future work that extends multiple-act to the natural language will also be discussed

A new bearing fault diagnosis method based on fine-to-coarse multiscale permutation entropy, Laplacian score and SVM

Fault diagnosis of rotating machinery is vital to identify incipient failures and avoid unexpected downtime in industrial systems. This paper proposes a new rolling bearing fault diagnosis method by integrating the fine-to-coarse multiscale permutation entropy (F2CMPE), Laplacian score (LS) and support vector machine (SVM). A novel entropy measure, named F2CMPE, was proposed by calculating permutation entropy via multiple-scale fine-grained and coarse-grained signals based on the wavelet packet decomposition. The entropy measure estimates the complexity of time series from both low- and high-frequency components. Moreover, the F2CMPE mitigates the drawback of producing time series with sharply reduced data length via the coarse-grained procedure in the conventional composite multiscale permutation entropy (CMPE). The comparative performance of the F2CMPE and CMPE is investigated by analyzing the synthetic and experimental signals for entropy-based feature extraction. In the proposed bearing fault diagnosis method, the F2CMPE is first used to extract the entropy-based features from bearing vibration signals. Then, LS and SVM are used for selection of features and fault classification, respectively. Finally, the effectiveness of the proposed method is verified for rolling bearing fault diagnosis using experimental vibration data sets, and the results have demonstrated the capability of the proposed method to recognize and identify the bearing fault patterns under different fault states and severity levels

Adaptive multiscale weighted permutation entropy for rolling bearing fault diagnosis

Fault diagnosis of rolling bearing is of great importance to ensure high reliability and safety in the industrial machinery system. Entropy measures are useful non-linear indicators for time series complexity analysis and have been widely applied in bearing fault diagnosis in the past decade. In this paper, an improved entropy measure is proposed, named Adaptive Multiscale Weighted Permutation Entropy (AMWPE). Then, a new rolling bearing fault diagnosis method is developed based on the AMWPE and multi-class SVM. For comparison, an experimental bearing dataset is analyzed using the AMWPE and conventional entropy measures, and then multi-class SVM is adopted for fault type classification. Further, the robustness of different entropy measures against noise is studied by analyzing noisy signals with various Signal-to-Noise Ratios (SNRs). The experimental results have demonstrated the effectiveness of the proposed method in bearing fault diagnosis under different fault types, severity degrees, and SNR levels

Edge permutation entropy: an improved entropy measure for time-series analysis

Permutation Entropy (PE) has been widely applied as a non-linear statistical indicator to estimate the change of complexity in time series. Though it is conceptually simple and computationally fast, PE encounters a few limitations. For example, the amplitude differences in time series are neglected, and the symbolic sequences generated from equal values are according to their emergence order. In this paper, an Edge Permutation Entropy (EdgePE) measure is proposed to improve the performance of PE, mainly overcoming its lack of ability to differentiate between amplitude differences in motifs that correspond to the same order pattern. The advantage of EdgePE relies on that amplitude change information can be identified and distinguished by the information underlying in the 'edge' distance between data points in the reconstructed embedded vectors. To demonstrate its improvement, the proposed EdgePE is compared with other related improved PE approaches, for analyzing synthetic time series and experimental rolling bearing data sets, respectively. The results indicate that the EdgePE can effectively characterize amplitude changes in time series (e.g., for spike and stuck detection) and improve the accuracy of pattern recognition for rolling bearing fault diagnosis, compared to those of the other related PE measures

Research of dimensionless index for fault diagnosis positioning based on EMD

Dimensionless index as a new theory tool has been applied in fault diagnosis study, which has shown some progress, however, it will cause some interference to the diagnosis results since no considering the influence of other noise jamming signal is given. Empirical Mode Decomposition (EMD) technique could extract effectively the fault characteristic signal of vibration data. In view of the noise jamming of dimensionless index in analyzing data, dimensionless index processing algorithms based on EMD is proposed. Firstly, EMD method is used to decompose the collected vibration signals, then the first few Intrinsic Mode Functions (IMF) components are obtained which contains the fault characteristic of vibration data, and the effects of other noise signal are removed at the same time. Secondly, fault diagnosis can be achieved by calculating dimensionless parameter values to the IMF components with characteristic signal of vibration data, and obtaining range of characteristic value of their dimensionless index, then diagnosing and analyzing fault characteristics of the equipment. The proposed method is applied to fault diagnosis test analysis of rotating machinery, and the experiment has shown that the proposed method is efficient and effective.</p

Impact of fouling on flow-induced vibration characteristics in fluid-conveying pipelines

This paper addresses monitoring problems commonly encountered in petrochemical enterprises caused by fouling and clogging in the circulating water heat exchangers by monitoring the heat exchanger’s wall vibration signal for early failure detection. Due to the difficulties encountered in simulation caused by the large number of tubes inside the heat exchanger, such methods were discussed by studying in the fluid-conveying pipeline fouling. ANSYS was used to establish the normal model and fouling model of a fluid-conveying pipeline so as to analyze the changing rule of various parameters that are influenced by different inlet velocities. As the inlet velocity and fouling severity continuously increased, the wall load and the vibration acceleration increased as well, leading variations in wall vibration signals. This paper conduct extensive experiments by using straight pipes to compare the results from simulation and from normal fluid-conveying pipelines, under the same working conditions. By such comparison, we estimate the accuracy of the simulation model.</p

Entropy measures in machine fault diagnosis: insights and applications

Entropy, as a complexity measure, has been widely applied for time series analysis. One preeminent example is the design of machine condition monitoring and industrial fault diagnostic systems. The occurrence of failures in a machine will typically lead to non-linear characteristics in the measurements, caused by instantaneous variations, which can increase the complexity in the system response. Entropy measures are suitable to quantify such dynamic changes in the underlying process, distinguishing between different system conditions. However, notions of entropy are defined differently in various contexts (e.g., information theory and dynamical systems theory), which may confound researchers in the applied sciences. In this paper, we have systematically reviewed the theoretical development of some fundamental entropy measures and clarified the relations among them. Then, typical entropy-based applications of machine fault diagnostic systems are summarized. Further, insights into possible applications of the entropy measures are explained, as to where and how these measures can be useful towards future data-driven fault diagnosis methodologies. Finally, potential research trends in this area are discussed, with the intent of improving online entropy estimation and expanding its applicability to a wider range of intelligent fault diagnostic systems

Entropy measures in machine fault diagnosis: insights and applications

Entropy, as a complexity measure, has been widely applied for time series analysis. One preeminent example is the design of machine condition monitoring and industrial fault diagnostic systems. The occurrence of failures in a machine will typically lead to non-linear characteristics in the measurements, caused by instantaneous variations, which can increase the complexity in the system response. Entropy measures are suitable to quantify such dynamic changes in the underlying process, distinguishing between different system conditions. However, notions of entropy are defined differently in various contexts (e.g., information theory and dynamical systems theory), which may confound researchers in the applied sciences. In this paper, we have systematically reviewed the theoretical development of some fundamental entropy measures and clarified the relations among them. Then, typical entropy-based applications of machine fault diagnostic systems are summarized. Further, insights into possible applications of the entropy measures are explained, as to where and how these measures can be useful towards future data-driven fault diagnosis methodologies. Finally, potential research trends in this area are discussed, with the intent of improving online entropy estimation and expanding its applicability to a wider range of intelligent fault diagnostic systems

A review of research on acoustic detection of heat exchanger tube

Leakage in heat exchanger tubes can result in unreliable products and dangerous situations, which could cause great economic losses. Along with fast development of modern acoustic detection technology, using acoustic signals to detect leakage in heat exchange tube has been gradually accepted and considered with great potential by both industrial and research societies. In order to further advance the development of acoustic signal detection technology and investigate better methods for leakage detection in heat exchange tube, in this paper, firstly, we conduct a short overview of the theory of acoustic signal detection on heat exchanger tube, which had already been continuously developed for a few decades by researchers worldwide. Thereafter, we further expound the advantages and limitations of acoustic signal detection technology on heat exchanger tube in four aspects: 1) principles of acoustic signal detection, 2) characteristics of sound wave propagation in heat exchanger tube, 3) methods of leakage detection, and 4) leakage localization in heat exchanger tube.</p

Boundary tracking of continuous objects based on binary tree structured SVM for industrial wireless sensor networks

Due to the flammability, explosiveness and toxicity of continuous objects (e.g., chemical gas, oil spill, radioactive waste) in the petrochemical and nuclear industries, boundary tracking of continuous objects is a critical issue for industrial wireless sensor networks (IWSNs). In this article, we propose a continuous object boundary tracking algorithm for IWSNs – which fully exploits the collective intelligence and machine learning capability within the sensor nodes. The proposed algorithm first determines an upper bound of the event region covered by the continuous objects. A binary tree-based partition is performed within the event region, obtaining a coarse-grained boundary area mapping. To study the irregularity of continuous objects in detail, the boundary tracking problem is then transformed into a binary classification problem; a hierarchical soft margin support vector machine training strategy is designed to address the binary classification problem in a distributed fashion. Simulation results demonstrate that the proposed algorithm shows a reduction in the number of nodes required for boundary tracking by at least 50%. Without additional fault-tolerant mechanisms, the proposed algorithm is inherently robust to false sensor readings, even for high ratios of faulty nodes (≈ 9%)