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.     

Model repair — aligning process models to reality

Process mining techniques relate observed behavior (i.e., event logs) to modeled behavior (e.g., a BPMN model or a Petri net). Process models can be discovered from event logs and conformance checking techniques can be used to detect and diagnose differences between observed and modeled behavior. Existing process mining techniques can only uncover these differences, but the actual repair of the model is left to the user and is not supported. In this paper we investigate the problem of repairing a process model w.r.t. a log such that the resulting model can replay the log (i.e., conforms to it) and is as similar as possible to the original model. To solve the problem, we use an existing conformance checker that aligns the runs of the given process model to the traces in the log. Based on this information, we decompose the log into several sublogs of non-fitting subtraces. For each sublog, either a loop is discovered that can replay the sublog or a subprocess is derived that is then added to the original model at the appropriate location. The approach is implemented in the process mining toolkit ProM and has been validated on logs and models from several Dutch municipalities.     

Geothermal energy utilization trends from a technological paradigm perspective

The use of geothermal energy and its associated technologies has been increasing worldwide. However, there has been little paradigmatic research conducted in this area. This paper proposes a systematic methodology to research the development trends for the sustainable development of geothermal energy. A novel data analysis system was created to research the geothermal energy utilization trends, and a technological paradigm theory was adopted to explain the technological changes. A diffusion velocity model was used to simulate and forecast the geothermal power generation development in the diffusion phase. Simulation results showed that the development of installed capacity for geothermal generation had a strong inertia force along with the S-curve. Power generation from geothermal power sources reached a peak in 2008 and is estimated to be saturated by 2030. Geothermal energy technologies in hybrid power systems based on other renewable energy sources look to be more promising in the future.     

Feasibility and effort estimation models for medium and small size information mining projects

Information mining is a sub-discipline of Information Systems which provides the non-trivial knowledge needed for making decisions inside an organization. Although such projects have different features from Software Engineering ones, they share some of their problems. Among these problems two are highlighted: unmanaged risks and inaccurate estimations of necessary resources to complete the project. In this context, this paper presents two ad-hoc models to be applied in Small and Medium-sized Enterprises: one for assessing project feasibility and the other for estimating the resources and time required to carry out the project. Both models should be applied at the beginning of the project.     

Simplifying the powder metallurgy manufacturing process using soft computing tools

The tools of soft computing will aid the knowledge mining in predicting and classifying the properties of various parameters while designing the composite preforms in the manufacturing of Powder Metallurgy (P/M) Lab. In this paper, an integrated PRNET (PCA-Radial basis functional neural NET) model is proposed in different versions to select the relevant parameters for preparing composite preforms and to predict the deformation and strain hardening properties of Al–Fe composites. It reveals that the predictability of this model has been increased by 67.89% relatively from the conventional models. A new PR-filter is proposed by slightly modifying the conventional filters of RBFNN, which improves the power of PRNET even though raw data are highly non-linear, interrelated and noisy. Moreover, fixing the range of input parameters for classifying the properties of composite preforms can be automated by the Fuzzy logic. These types of models will avoid expensive experimentation and risky environment while preparing sintered composite preforms. Thus the manufacturing process of composites in P/M Lab will be simplified with minimum energy by the support of these soft-computing tools.     

Bayesian network construction from event log for lateness analysis in port logistics

The handling of containers in port logistics consists of several activities, such as discharging, loading, gate-in and gate-out, among others. These activities are carried out using various equipment including quay cranes, yard cranes, trucks, and other related machinery. The high inter-dependency among activities and equipment on various factors often puts successive activities off schedule in real-time, leading to undesirable activity down time and the delay of activities. A late container process, in other words, can negatively affect the scheduling of the following ones. The purpose of the study is to analyze the lateness probability using a Bayesian network by considering various factors in container handling. We propose a method to generate a Bayesian network from a process model which can be discovered from event logs in port information systems. In the network, we can infer the activities’ lateness probabilities and, sequentially, provide to port managers recommendations for improving existing activities.     

矿用可移动式救生舱的构成及安放

该文对矿用可移动式救生舱进行了分类,对比了各自的优缺点,指出国内厂家和高校均以硬体式救生舱作为主要研究对象;随后着重对此种救生舱的总体构成进行了说明,介绍了过渡舱、生活舱和设备舱的具体功能和内部设备;最后论述了几种固定和安放方式的利弊,指出在实际应用中,应根据所在巷道的地质特点合理选择安置方案。     

块体理论在坚硬特厚煤层综放开采中的应用探索

坚硬特厚煤层采用综放开采时,顶煤受到节理裂隙面的截割,将形成相互耦合的块体,当顶煤下方形成临空面时,部分顶煤块体将首先出现垮落。针对此情况,结合现场钻孔窥视运用块体理论研究方法,分析顶煤体受到的截割状况。从块体沿着单一节理面的垮落和双节理面的垮落类型出发,分析其运动关系和动力关系,得到块体滑动垮落与节理面内摩擦角,及其与滑动垮落方向的关系式,为坚硬特厚煤层顶煤垮落分析提供新的研究角度。     

里必井田承压水带压开采评估

煤层底板承压水突出是发生水害事故的原因之一,要减少该类事故的发生,需对底板突水危险性进行准确评价。为保障里必井田承压水带压开采的安全进行,基于煤层底板突水研究理论分析,采用常规突水系数法和等效厚度突水系数法,分别对里必井田内3#煤和15#煤的奥灰水突水危险性进行了研究。研究发现,奥灰水对于3#煤的开采影响不大,但在局部构造及陷落柱发育地段及采区东北部区域仍存在一定的突水可能性;奥灰水对15#煤开采具有较大威胁,大部分区域具有较大突水危险性。等效岩层厚度法计算的突水系数考虑了不同性质岩层的岩石强度和阻水能力,计算更接近生产实际,结果更可靠;且在对突水系数法进一步优化后,其评价的结果可以更精确。     

地质构造及局部大倾角条件下综放工作面安全回采关键技术研究与应用

断层等地质构造是制约矿井安全生产的重要因素,尤其是在大倾角、大落差断层等条件下,片帮、漏冒顶等将会严重影响安全生产.针对大平矿3111工作面在最大倾角达30°条件下,通过三机防滑、注浆、爆破、调斜等措施,采取"硬过"的方式在大倾角条件下工作面顺利通过了大落差断层,取得了较好的实践效果,为矿井积累了宝贵的回采经验.