Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: A deep reinforcement learning approach

Fault diagnosis methods for rotating machinery have always been a hot research topic, and artificial intelligence-based approaches have attracted increasing attention from both researchers and engineers. Among those related studies and methods, artificial neural networks, especially deep learning-based methods, are widely used to extract fault features or classify fault features obtained by other signal processing techniques. Although such methods could solve the fault diagnosis problems of rotating machinery, there are still two deficiencies. (1) Unable to establish direct linear or non-linear mapping between raw data and the corresponding fault modes, the performance of such fault diagnosis methods highly depends on the quality of the extracted features. (2) The optimization of neural network architecture and parameters, especially for deep neural networks, requires considerable manual modification and expert experience, which limits the applicability and generalization of such methods. As a remarkable breakthrough in artificial intelligence, AlphaGo, a representative achievement of deep reinforcement learning, provides inspiration and direction for the aforementioned shortcomings. Combining the advantages of deep learning and reinforcement learning, deep reinforcement learning is able to build an end-to-end fault diagnosis architecture that can directly map raw fault data to the corresponding fault modes. Thus, based on deep reinforcement learning, a novel intelligent diagnosis method is proposed that is able to overcome the shortcomings of the aforementioned diagnosis methods. Validation tests of the proposed method are carried out using datasets of two types of rotating machinery, rolling bearings and hydraulic pumps, which contain a large number of measured raw vibration signals under different health states and working conditions. The diagnosis results show that the proposed method is able to obtain intelligent fault diagnosis agents that can mine the relationships between the raw vibration signals and fault modes autonomously and effectively. Considering that the learning process of the proposed method depends only on the replayed memories of the agent and the overall rewards, which represent much weaker feedback than that obtained by the supervised learning-based method, the proposed method is promising in establishing a general fault diagnosis architecture for rotating machinery.     

Using modern technology to keep in touch with back burners: An investment model analysis

Relationship research and theory recognizes that individuals continue to monitor the availability of their romantic/sexual prospects whether or not they are already in a committed relationship. We use the term back burner to describe a desired potential or continuing romantic/sexual partner with whom one communicates, but to whom one is not exclusively committed. Although communication with back burners is not new, modern technology affords novel channels (e.g., social networking applications and text messaging) that individuals are using to connect with back burners. A survey study (N = 374) explored whether people used technology to communicate with back burners, as well as relationships between back burner contacts and investment model variables (Rusbult, 1980). Results indicated that back burner activity through electronic channels was common, men reported more back burners than women, and that number of back burners associated positively with quality of alternatives. For those in committed relationships, no relationships were observed between back burner activity and commitment to or investment in the relationship. Implications and limitations are discussed.     

Assessment of back load in assemblyline work using electromyography

Abstract

When the back is loaded, the amplitude of the myoelectric signals in the lumbar region of the back has been found to correlate well to the compression force in the spine measured by means of disc pressure. The purpose of this paper is to use surface electromyography to quantify the load on the back in several working situations.

Thirteen male workers participated in a study of three strenuous workstations along the assembly line of a car factory; mounting of a side panel and a sound insulator in the front compartment, mounting of floor mats, and mounting of the left front seat. Electrodes were placed at the level T8, LI and L3 on both sides of the spine.

The average load during a work cycle is given as mean values of signal amplitude. Standard deviations are calculated to indicate the average load variations. Amplitude histograms are also presented to illustrate the loading pattern in more detail.

The results show that it is possible to record myoelectric signals at the workplace without serious disturbance. The amplitude levels were high at all three workstations. The actual way in which each task was carried out resulted in differences in activity level between the thoracic and lumbar regions of the back which could be used to identify incorrect body postures or work activities. The use of a lifting aid at one workstation was found to give a significant decrease in high amplitude levels. Analysis of myoelectric activity patterns give useful guidance about how to reduce body loading in heavy work situations and also permits quantitative evaluation of such improvements.     

Aspect based sentiment analysis using deep learning approaches: A survey

The wealth of unstructured text on the online web portal has made opinion mining the most thrust area for researchers, academicians, and businesses to extract information for gathering, analyzing, and aggregating human emotions. The extraction of public sentiment from the text at an aspect level has contributed exceptionally to various businesses in the marketplace. In recent times, deep learning-based techniques have learned high-level linguistic features without high-level feature engineering. Therefore, this paper focuses on a rigorous survey on two primary subtasks, aspect extraction and aspect category detection of aspect-based sentiment analysis (ABSA) methods based on deep learning. The significant advancement in the ABSA sector is demonstrated by a thorough evaluation of state-of-the-art and latest aspect extraction methodologies.     

Real-time scheduling for distributed permutation flowshops with dynamic job arrivals using deep reinforcement learning

Distributed manufacturing plays an important role for large-scale companies to reduce production and transportation costs for globalized orders. However, how to real-timely and properly assign dynamic orders to distributed workshops is a challenging problem. To provide real-time and intelligent decision-making of scheduling for distributed flowshops, we studied the distributed permutation flowshop scheduling problem (DPFSP) with dynamic job arrivals using deep reinforcement learning (DRL). The objective is to minimize the total tardiness cost of all jobs. We provided the training and execution procedures of intelligent scheduling based on DRL for the dynamic DPFSP. In addition, we established a DRL-based scheduling model for distributed flowshops by designing suitable reward function, scheduling actions, and state features. A novel reward function is designed to directly relate to the objective. Various problem-specific dispatching rules are introduced to provide efficient actions for different production states. Furthermore, four efficient DRL algorithms, including deep Q-network (DQN), double DQN (DbDQN), dueling DQN (DlDQN), and advantage actor-critic (A2C), are adapted to train the scheduling agent. The training curves show that the agent learned to generate better solutions effectively and validate that the system design is reasonable. After training, all DRL algorithms outperform traditional meta-heuristics and well-known priority dispatching rules (PDRs) by a large margin in terms of solution quality and computation efficiency. This work shows the effectiveness of DRL for the real-time scheduling of dynamic DPFSP.     

CMOS兼容的ISFET传感器模型和测量电路研究

随着CMOS工艺的不断发展,将离子敏场效应晶体管(ISFET)传感器与CMOS技术相结合,以达到提高集成度、降低成本、减小系统尺寸、提高系统可靠性。在对与CMOS工艺兼容的ISFET传感器结构模型分析的基础上,研究了一种测量电路,它具有有利于消除体效应的影响、减少共模噪声的影响、克服温度漂移等优点。对该测量电路进行模拟仿真,得到输出电压与pH值之间的关系图,结果表明:其结果与理论模型仿真值基本吻合。     

深层网技术在专利数据提取中的应用

深层网技术是获取隐藏在以表单为特征的网络数据库检索入口后的数据页面的提取技术,解决了通用搜索引擎不能有效索引深层网网页的问题。专利数据是一类重要的深层网数据资源,对其进行提取、挖掘具有重要意义。本文利用深层网技术开发了一个专利检索系统,实现了对中国和美国专利数据的本地检索、提取和下载,以及针对中国专利的法律状态检索。该软件支持专利的批量下载及文件管理,并根据中国和美国专利显示为多个单页TIFF格式图片不利于本地管理和浏览的特点,开发了实用性较强的TIFF格式图片多页合并和通用PDF格式转换功能。该专利检索系统采用面向用户的界面设计和功能开发,具有简单、易用的特点。     

Securing social platform from misinformation using deep learning

People are easily duped by fake news and start to share it on their networks. With high frequency, fake news causes panic and forces people to engage in unethical behavior such as strikes, roadblocks, and similar actions. Thus, counterfeit news detection is highly needed to secure people from misinformation on social platforms. Filtering fake news manually from social media platforms is nearly impossible, as such an act raises security and privacy concerns for users. As a result, it is critical to assess the quality of news early on and prevent it from spreading. In this article, we propose an automated model to identify fake news at an early stage. Machine learning-based models such as Random Forest, Logistic Regression, Naïve Bayes, and K-Nearest Neighbor are used as baseline models, implemented with the features extracted using countvectorizer and tf–idf. The baseline and other existing model outcomes are compared with the proposed deep learning-based Long–Short Term Memory (LSTM) network. Experimental results show that different settings achieved an accuracy of 99.82% and outperformed the baseline and existing models.     

Effect of prescribed sleep surfaces on back pain and sleep quality in patients diagnosed with low back and shoulder pain

The purpose of this study was to assess sleep quality and comfort of participants diagnosed with low back pain and stiffness following sleep on individually prescribed mattresses based on dominant sleeping positions. Subjects consisted of 27 patients (females, n = 14; males, n = 13; age 44.8 yrs ± SD 14.6, weight 174 lb. ±SD 39.6, height 68.3 in. ± SD 3.7) referred by chiropractic physicians for the study. For the baseline (pretest) data subjects recorded back and shoulder discomfort, sleep quality and comfort by visual analog scales (VAS) for 21 days while sleeping in their own beds. Subsequently, participants’ beds were replaced by medium-firm mattresses specifically layered with foam and latex based on the participants’ reported prominent sleeping position and they again rated their sleep comfort and quality daily for the following 12 weeks. Analysis yielded significant differences between pre- and post means for all variables and for back pain, we found significant (p < 0.01) differences between the first posttest mean and weeks 4 and weeks 8-12, thus indicating progressive improvement in both back pain and stiffness while sleeping on the new mattresses. Additionally, the number of days per week of experiencing poor sleep and physical discomfort decreased significantly. It was concluded that sleep surfaces are related to sleep discomfort and that is indeed possible to reduce pain and discomfort and to increase sleep quality in those with chronic back pain by replacing mattresses based on sleeping position.     

Automatic thesaurus construction for spam filtering using revised back propagation neural network

Email has become one of the fastest and most economical forms of communication. Email is also one of the most ubiquitous and pervasive applications used on a daily basis by millions of people worldwide. However, the increase in email users has resulted in a dramatic increase in spam emails during the past few years. This paper proposes a new spam filtering system using revised back propagation (RBP) neural network and automatic thesaurus construction. The conventional back propagation (BP) neural network has slow learning speed and is prone to trap into a local minimum, so it will lead to poor performance and efficiency. The authors present in this paper the RBP neural network to overcome the limitations of the conventional BP neural network. A well constructed thesaurus has been recognized as a valuable tool in the effective operation of text classification, it can also overcome the problems in keyword-based spam filters which ignore the relationship between words. The authors conduct the experiments on Ling-Spam corpus. Experimental results show that the proposed spam filtering system is able to achieve higher performance, especially for the combination of RBP neural network and automatic thesaurus construction.     

Predictive maintenance using cox proportional hazard deep learning

Predictive maintenance (PdM) has become prevalent in the industry in order to reduce maintenance cost and to achieve sustainable operational management. The core of PdM is to predict the next failure so corresponding maintenance can be scheduled before it happens. The purpose of this study is to establish a Time-Between-Failure (TBF) prediction model through a data-driven approach. For PdM, data sparsity is regarded as a critical issue which can jeopardize algorithm performance for the modelling based on maintenance data. Meanwhile, data censoring has imposed another challenge for handling maintenance data because the censored data is only partially labelled. Furthermore, data sparsity may affect algorithm performance of existing approaches when addressing the data censoring issue. In this study, a new approach called Cox proportional hazard deep learning (CoxPHDL) is proposed to tackle the aforementioned issues of data sparsity and data censoring that are common in the analysis of operational maintenance data. The idea is to offer an integrated solution by taking advantage of deep learning and reliability analysis. To start with, an autoencoder is adopted to convert the nominal data into a robust representation. Secondly, a Cox proportional hazard model (Cox PHM) is researched to estimate the TBF of the censored data. A long-short-term memory (LSTM) network is then established to train the TBF prediction model based on the pre-processed maintenance data. Experimental studies using a sizable real-world fleet maintenance data set provided by a UK fleet company have demonstrated the merits of the proposed approach where the algorithm performance based on the proposed LSTM network has been improved respectively in terms of MCC and RMSE.     

A context-aware sensing strategy with deep reinforcement learning for smart healthcare

Health sensing system (HSS), offering a variety of health services, has attracted considerable research attention in the area of smart healthcare. However, continuous sensing inevitably brings dramatic energy consumption of mobile sensing devices. On the other hand, the reduction of sensing time duration causes excessive delay in sensing a user state change and the missing of critical physiologic signal. Thus, the trade-off between energy consumption and delay constitutes a primary challenge in the design of HSS. In this paper, we propose an adaptive sensing strategy to intelligently determine the trigger time for sensing physiological parameters at a HSS. Furthermore, human context recognition (HCR) is adopted to design context-aware sensing strategy, where the health condition, sensing requirements, and dependence on physiological data are considered simultaneously. To devise the sensing strategy, we first generate a dynamic observation model. Next, we propose a sort retention double-DQN based sensing strategy. In comparison to traditional double-DQN, the proposed approach can effectively enhance learning stability and sample efficiency. With SRD-DQN, we can obtain the optimized solution for the schedule of the successive window according to the current state. We implement blood pressure and heart rate monitoring simulations to evaluate the performance of the proposed sensing strategy. Simulation results reveal that the sensing strategy can effectively restrain energy consumption and delay, and SRD-DQN converges faster than traditional DQN.     

The role of whole-body vibration in back pain: A cross-sectional study with agricultural pilots

ObjectiveA cross-sectional study was conducted to investigate the role of whole-body vibration as a risk factor for spinal musculoskeletal symptoms among agricultural pilots.MethodThe study was conducted in two stages that included measuring the pilots’ exposure to whole-body vibration during the flight procedures and applying a self-administered questionnaire about musculoskeletal symptoms of the spine.ResultsNone of the four aircraft texted exposed the pilot above the Exposure Limit Value (ELV) established by the standards. However, in a few specific situations, two of them exceeded the Exposure Action Value (EAV). About 62% of the pilots who operated these aircraft reported some musculoskeletal symptoms of the spine in the last few 12 months.ConclusionUsing the data from this study, it was possible to calculate the odds ratio of the agricultural pilot suffering low back pain, based on eight personal and work-related factors.Relevance for the industryBased on the results of the present study, it was possible to define strategies to reduce whole-body exposure in agricultural aircraft and, consequently, improve the pilots’ health. Strategies included management of the exposure and aircraft improvement.     

Passivity analysis for uncertain BAM neural networks with time delays and reaction–diffusions

This article deals with the problem of passivity analysis for delayed reaction–diffusion bidirectional associative memory (BAM) neural networks with weight uncertainties. By using a new integral inequality, we first present a passivity condition for the nominal networks, and then extend the result to the case with linear fractional weight uncertainties. The proposed conditions are expressed in terms of linear matrix inequalities, and thus can be checked easily. Examples are provided to demonstrate the effectiveness of the proposed results.     

A deep supervised approach for ischemic lesion segmentation from multimodal MRI using Fully Convolutional Network

The principle restorative step in the treatment of ischemic stroke depends on how fast the lesion is delineated from the Magnetic Resonance Imaging (MRI) images. This will serve as a vital aid to estimate the extent of damage caused to the brain cells. However, manual delineation of the lesion is time-consuming and it is subjected to intra-observer and inter-observer variability. Most of the existing methods for ischemic lesion segmentation rely on extracting handcrafted features followed by application of a machine learning algorithm. Identifying such features demand multi-domain expertise in Neuro-radiology as well as Image processing. This can be accomplished by learning the features automatically using Convolutional Neural Network (CNN). To perform segmentation, the spatial arrangement of pixel needs to be preserved in addition to learning local features of an image. Hence, a deep supervised Fully Convolutional Network (FCN) is presented in this work to segment the ischemic lesion. The highlight of this research is the application of Leaky Rectified Linear Unit activation in the last two layers of the network for a precise reconstruction of the ischemic lesion. By doing so, the network was able to learn additional features which are not considered in the existing U-Net architecture. Also, an extensive analysis was conducted in this research to select optimal hyper-parameters for training the FCN. A mean segmentation accuracy of 0.70 has been achieved from the experiments conducted on ISLES 2015 dataset. Experimental observations show that our proposed FCN method is 10% better than the existing works in terms of Dice Coefficient.     

A novel deep learning prediction model for concrete dam displacements using interpretable mixed attention mechanism

Dam displacements can effectively reflect its operational status, and thus establishing a reliable displacement prediction model is important for dam health monitoring. The majority of the existing data-driven models, however, focus on static regression relationships, which cannot capture the long-term temporal dependencies and adaptively select the most relevant influencing factors to perform predictions. Moreover, the emerging modeling tools such as machine learning (ML) and deep learning (DL) are mostly black-box models, which makes their physical interpretation challenging and greatly limits their practical engineering applications. To address these issues, this paper proposes an interpretable mixed attention mechanism long short-term memory (MAM-LSTM) model based on an encoder-decoder architecture, which is formulated in two stages. In the encoder stage, a factor attention mechanism is developed to adaptively select the highly influential factors at each time step by referring to the previous hidden state. In the decoder stage, a temporal attention mechanism is introduced to properly extract the key time segments by identifying the relevant hidden states across all the time steps. For interpretation purpose, our emphasis is placed on the quantification and visualization of factor and temporal attention weights. Finally, the effectiveness of the proposed model is verified using monitoring data collected from a real-world dam, where its accuracy is compared to a classical statistical model, conventional ML models, and homogeneous DL models. The comparison demonstrates that the MAM-LSTM model outperforms the other models in most cases. Furthermore, the interpretation of global attention weights confirms the physical rationality of our attention-based model. This work addresses the research gap in interpretable artificial intelligence for dam displacement prediction and delivers a model with both high-accuracy and interpretability.     

Early-stage atherosclerosis detection using deep learning over carotid ultrasound images

This paper proposes a computer-aided diagnosis tool for the early detection of atherosclerosis. This pathology is responsible for major cardiovascular diseases, which are the main cause of death worldwide. Among preventive measures, the intima-media thickness (IMT) of the common carotid artery stands out as early indicator of atherosclerosis and cardiovascular risk. In particular, IMT is evaluated by means of ultrasound scans. Usually, during the radiological examination, the specialist detects the optimal measurement area, identifies the layers of the arterial wall and manually marks pairs of points on the image to estimate the thickness of the artery. Therefore, this manual procedure entails subjectivity and variability in the IMT evaluation. Instead, this article suggests a fully automatic segmentation technique for ultrasound images of the common carotid artery. The proposed methodology is based on machine learning and artificial neural networks for the recognition of IMT intensity patterns in the images. For this purpose, a deep learning strategy has been developed to obtain abstract and efficient data representations by means of auto-encoders with multiple hidden layers. In particular, the considered deep architecture has been designed under the concept of extreme learning machine (ELM). The correct identification of the arterial layers is achieved in a totally user-independent and repeatable manner, which not only improves the IMT measurement in daily clinical practice but also facilitates the clinical research. A database consisting of 67 ultrasound images has been used in the validation of the suggested system, in which the resulting automatic contours for each image have been compared with the average of four manual segmentations performed by two different observers (ground-truth). Specifically, the IMT measured by the proposed algorithm is 0.625 ± 0.167 mm (mean ± standard deviation), whereas the corresponding ground-truth value is 0.619 ± 0.176 mm. Thus, our method shows a difference between automatic and manual measures of only 5.79 ± 34.42 μm. Furthermore, different quantitative evaluations reported in this paper indicate that this procedure outperforms other methods presented in the literature.     

Long-time gap crowd prediction using time series deep learning models with two-dimensional single attribute inputs

Crowd prediction is a crucial aspect of modern life with innumerable applications. By predicting future human occupancy in advance, crowd prediction can support the decision-making processes of facility stakeholders, e.g., the campus operator can schedule facility maintenance during the period of lowest pedestrian flow to eliminate any disturbance. Conventional crowd prediction utilizes statistical models and rule-based data mining techniques, which are tedious in data processing and error-prone. Hence, this study formulates crowd prediction into a time-series analysis based on deep learning. Despite its wide adaptability in various research fields, deep learning-based time series analysis is seldom adopted in crowd prediction. There are two major limitations in previous studies: firstly, the prediction accuracy notably degrades with increased prediction length, and secondly only the temporal pattern along a single time dimension is exploited, i.e., the consecutive time steps in the most recent input data. Therefore, a Long-Time Gap Two-Dimensional method, entitled LT2D-method, is proposed to increase the crowd prediction length of with high accuracy. The LT2D-method is composed of two parts, (1) long-time gap prediction, which extends the prediction length to 240 time steps (1 day) with high accuracy, and (2) 2D inputs method, which exploits the prior knowledge from different time dimensions to further improve the prediction accuracy of long-time gap prediction. The proposed LT2D-method can be generally adapted to deep learning models, such as LSTM, BiLSTM, and GRU, to improve the prediction accuracy. By incorporating the proposed LT2D-method into different baseline models, the accuracy is generally improved by around 22%, demonstrating the robustness and generalizability of our method.     

Intelligent fault recognition framework by using deep reinforcement learning with one dimension convolution and improved actor-critic algorithm

The quality of fault recognition part is one of the key factors affecting the efficiency of intelligent manufacturing. Many excellent achievements in deep learning (DL) have been realized recently as methods of fault recognition. However, DL models have inherent shortcomings. In particular, the phenomenon of over-fitting or degradation suggests that such an intelligent algorithm cannot fully use its feature perception ability. Researchers have mainly adapted the network architecture for fault diagnosis, but the above limitations are not taken into account. In this study, we propose a novel deep reinforcement learning method that combines the perception of DL with the decision-making ability of reinforcement learning. This method enhances the classification accuracy of the DL module to autonomously learn much more knowledge hidden in raw data. The proposed method based on the convolutional neural network (CNN) also adopts an improved actor-critic algorithm for fault recognition. The important parts in standard actor-critic algorithm, such as environment, neural network, reward, and loss functions, have been fully considered in improved actor-critic algorithm. Additionally, to fully distinguish compound faults under heavy background noise, multi-channel signals are first stacked synchronously and then input into the model in the end-to-end training mode. The diagnostic results on the compound fault of the bearing and tool in the machine tool experimental system show that compared with other methods, the proposed network structure has more accurate results. These findings demonstrate that under the guidance of the improved actor-critic algorithm and processing method for multi-channel data, the proposed method thus has stronger exploration performance.