Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

Machine fault diagnosis with small sample based on variational information constrained generative adversarial network

In actual engineering scenarios, limited fault data leads to insufficient model training and over-fitting, which negatively affects the diagnostic performance of intelligent diagnostic models. To solve the problem, this paper proposes a variational information constrained generative adversarial network (VICGAN) for effective machine fault diagnosis. Firstly, by incorporating the encoder into the discriminator to map the deep features, an improved generative adversarial network with stronger data synthesis capability is established. Secondly, to promote the stable training of the model and guarantee better convergence, a variational information constraint technique is utilized, which constrains the input signals and deep features of the discriminator using the information bottleneck method. In addition, a representation matching module is added to impose restrictions on the generator, avoiding the mode collapse problem and boosting the sample diversity. Two rolling bearing datasets are utilized to verify the effectiveness and stability of the presented network, which demonstrates that the presented network has an admirable ability in processing fault diagnosis with few samples, and performs better than state-of-the-art approaches.     

Resveratrol as Chemosensitizer Agent: State of Art and Future Perspectives

Resistance to chemotherapy still remains a major challenge in the clinic, impairing the quality of life and survival rate of patients. The identification of unconventional chemosensitizing agents is therefore an interesting aspect of cancer research. Resveratrol has emerged in the last decades as a fascinating molecule, able to modulate several cancer-related molecular mechanisms, suggesting a possible application as an adjuvant in cancer management. This review goes deep into the existing literature concerning the possible chemosensitizing effect of resveratrol associated with the most conventional chemotherapeutic drugs. Despite the promising effects observed in different cancer types in in vitro studies, the clinical translation still presents strong limitations due to the low bioavailability of resveratrol. Recently, efforts have been moved in the field of drug delivery to identifying possible strategies/formulations useful for a more effective administration. Despite the necessity of a huge implementation in this research area, resveratrol appears as a promising molecule able to sensitize resistant tumors to drugs, suggesting its potential use in therapy-refractory cancer patients.     

Decreased CD10-positive granulocytes for the differential diagnosis of myelodysplastic syndrome

Myelodysplastic syndromes (MDS) are highly heterogeneous myeloid neoplasms, and a large number of patients are difficult to diagnose and classify by blood and bone marrow examination. As a surface marker of granulocyte, studies have shown CD10 can be used to define the degree of granulocyte maturation in MDS patients. However, whether it can be used for differential diagnosis of MDS and other hematological diseases remains inconclusive. To explore the value of CD10 for differential diagnosis of MDS, 60 newly diagnosed MDS, 20 aplastic anemia (AA) patients, and 35 iron-deficient anemia (IDA) patients were selected for this study. Bone marrow (BM) specimens were processed for surface marker analysis and labeled with pre-conjugated monoclonal antibodies. Stained cells were detected by flow cytometry. Our results indicated that CD10-positive granulocytes were significantly decreased in BM of MDS patients than AA and IDA patients, and the level of CD10-positive mature granulocytes was not associated with the clinical stages of malignancy. Receiver operating characteristic (ROC) areas under the curve (AUC) of CD10-positive granulocytes was 0.86 and 0.85, respectively, in MDS patients than the IDA group and AA group with good specificity and sensitivity. Further, CD10-positive granulocytes were increased after effective treatment. In conclusion, we found the decrease in CD10-positive granulocytes has a differential diagnostic value of MDS.     

基于残差决策的自动飞行控制系统远程实时故障诊断

针对自动飞行控制系统结构复杂、关联部件众多,发生故障时诊断时间长,从而影响飞机运行效率的问题,提出一种基于飞机通信寻址报告系统(ACARS)的远程实时故障诊断方案。首先,分析自动飞行控制系统的故障特点,设计搭建检测滤波器;然后,利用ACARS数据链实时发送的自动飞行控制系统的关键信息进行相关部件的残差计算,并根据残差决策算法进行故障诊断及定位;最后,针对不同故障部件残差间的差异大、决策门限无法统一的缺点,提出基于二次差值的残差决策改进算法,减缓了检测对象的整体变化趋势,降低了随机噪声和干扰的影响,避免了将瞬态故障诊断为系统故障的情况。实验仿真结果表明,基于二次差值的改进残差决策算法避免了多决策门限的复杂性,在采样时间为0.1 s的情况下,故障检测所需时间大约为2 s,故障检测时间大幅降低,有效故障检测率大于90%。     

Sensor network design based on system-wide reliability criteria. Part I: Objectives

Reliability based criteria are quite popular for optimal sensor network design. We present a modified definition of system reliability for sensor network design for two applications: reliable estimation of variables in a steady state linear flow process, and reliable fault detection and diagnosis for any process. Unlike the weakest-link based definition of system reliability in the literature, the proposed definition considers the entire system and is consistent with the reliability concept used in classical reliability literature. For each application, dual approaches for defining system reliability are proposed, and their analogy with the reliability problem in the classical reliability literature is established. Using examples and stochastic simulations, the advantage of using the proposed system reliability in contrast to the existing definition is illustrated. Part II of this series of articles presents methods for efficient generation of the system reliability function and its use in optimization-based approaches for designing optimal sensor networks.     

高校实验室计算机网络故障诊断技术研究

为了提升高校实验数据处理水平,高校实验室的相关建设工作已经步入正轨,但层出不穷的信息安全问题也向高校实验室计算机网络维护工作提出了新的挑战。文章从现阶段高校实验室计算机网络的相关建设工作出发,简要论述引发网络故障的具体原因,并对其诊断技术进行探讨。     

气管上段腺样囊性癌的临床病理

目的　探讨气管上段腺样囊性癌的临床病理特征及诊断和鉴别诊断要点。方法　收集2000年1月至2014年2月在北京协和医院确诊的4例气管上段腺样囊性癌病例，通过光镜、免疫组织化学及组织化学染色分析其临床病理特征、免疫组织化学特点、诊断及鉴别诊断要点。结果　4例气管上段腺样囊性癌患者中，男1例，女3例，平均年龄47岁(38~57岁）；既往均无腺样囊性癌病史，1例患者既往有结节性甲状腺肿手术史。镜下检查4例均为筛状/管状型腺样囊性癌，3例累及甲状腺组织，3例累及神经组织，未见淋巴结受累。免疫组织化学染色示4例P16、CD117、BCL2、P63、SMA均阳性，Ki67指数平均8%，TTF1和P53均阴性；4例中基底膜样物胶原Ⅳ阳性；组织化学染色示AB/PAS阳性。术后均接受总剂量为48~56 Gy的放疗，随访6~120个月，平均72.5个月，1例术后96个月复发，3例随访期间无复发及转移。结论　气管上段腺样囊性癌是罕见的原发于气管的低度恶性肿瘤，肿瘤生长缓慢，就诊时多数已侵及甲状腺组织，需要与原发于甲状腺的恶性肿瘤相鉴别，特别是在甲状腺穿刺及术中冰冻检查时。结合电子喉气管镜下表现、典型的形态学及免疫组织化学和组织化学染色有助于准确诊断。手术难以切除干净，术后放疗对延缓疾病复发有一定帮助。     

An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.     

A precise sensor fault detection technique using statistical techniques for wireless body area networks

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.