综合改进奇异谱分解和奇异值分解的齿轮故障特征提取方法

针对齿轮故障特征微弱，在强背景噪声下难以有效提取的问题，提出了一种改进奇异谱分解（ISSD）结合奇异值分解（SVD）的齿轮故障特征提取方法。针对奇异谱分解（SSD）算法中模态参数需凭经验选取的缺陷，基于散布熵优化算法对SSD算法进行了改进，在得到既定的一组奇异谱分量的基础上，根据峭度值最大准则筛选出了最佳奇异谱分量并进行了SVD处理，采用奇异值能量标准谱自适应地确定了信号重构阶数以还原信号和提高降噪效果。最后对信号进行包络解调以提取齿轮故障特征，将所提方法运用到仿真信号和齿轮实测信号中，并同传统包络谱、SSD包络谱以及经验模态分解结合SVD(EMD-SVD)方法进行了对比分析，结果表明，所提方法的降噪和特征提取效果更佳，能够更加有效地实现齿轮故障的判别。     

Online monitoring of the curing of adhesives with a miniaturised interdigital sensor using impedance spectroscopy

Monitoring adhesives during manufacturing and their lifetime has become increasingly important due to the variety of materials and applications. Impedance spectroscopy is a suitable method for online monitoring of the curing process. We present a miniaturised interdigital structure to monitor the curing process of the adhesives using impedance spectroscopy. Compared to other sensors, our sensor is ultrathin, so that it disappears in the adhesive joint. Therefore, it can remain in the joint and be used for lifetime monitoring. In addition it is suitable for thin adhesive layers due to its fine grid. We demonstrate that the impedance of the sensor embedded in the adhesive gives insight into the curing mechanisms. Therefore monitoring of a dispersion and an epoxy is shown. In addition, the curing cycles can be reliably controlled using this monitoring method. The permittivity of the adhesives is extracted from the impedance measurements, applying analytical models of the electrical field of the interdigital structure.     

Magnetic induction based cluster optimization in non-conventional WSNs: A cross layer approach

Conventional Radio Frequency (RF) communication technique is unsuitable for communication in non-conventional media (water, soil, rocks, etc.) because of heavy losses incurred due to dynamic channel characteristics. Magnetic Induction (MI) communication overcomes these losses as it is least affected by such varying channel characteristics. In non-conventional media based Wireless Sensor Networks (WSNs) the deployed sensor nodes cannot replace or replenish their batteries. Thus, energy consumption should be minimized and that can be achieved by clustering process. This process involves data sensing, aggregation and routing to the base station. These sub-tasks are performed under Physical (PHY), Medium Access Control (MAC) and Network (NET) layers of OSI Network model. Having lesser or larger number of clusters has different impact on energy consumption in different layers’ perspective. A large number of clusters decreases energy consumption as per PHY layer whereas it results in increased energy consumption as per MAC and NET layers. Thus, a trade-off is required to minimize the overall energy consumption. To this end, we found an optimal number of clusters considering the simultaneous influence of all three layers. The above analysis is performed for three media viz. sea water, fresh water and dry soil.     

Scalable Distributed Sensor Fault Diagnosis for Smart Buildings

The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve occupants’ productivity and health, prioritize Smart Buildings as an emerging technology. The Heating, Ventilation and Air-Conditioning (HVAC) system is considered one of the most critical and essential parts in buildings since it consumes the largest amount of energy and is responsible for humans comfort. Due to the intermittent operation of HVAC systems, faults are more likely to occur, possibly increasing eventually building’s energy consumption and/or downgrading indoor living quality. The complexity and large scale nature of HVAC systems complicate the diagnosis of faults in a centralized framework. This paper presents a distributed intelligent fault diagnosis algorithm for detecting and isolating multiple sensor faults in large-scale HVAC systems. Modeling the HVAC system as a network of interconnected subsystems allows the design of a set of distributed sensor fault diagnosis agents capable of isolating multiple sensor faults by applying a combinatorial decision logic and diagnostic reasoning. The performance of the proposed method is investigated with respect to robustness, fault detectability and scalability. Simulations are used to illustrate the effectiveness of the proposed method in the presence of multiple sensor faults applied to a 83-zone HVAC system and to evaluate the sensitivity of the method with respect to sensor noise variance.      

综合案例设计在“传感器与测试技术”课程教学中的应用

“传感器与测试技术”涉及多学科交叉，是“通专融合、跨界培养”为特征的新工科人才培养中相关专业的重要课程。在非电类专业本科生所学相应基础知识有限的情况下，如何利用有限学时达到最佳教学效果，是本课程面临的一个挑战。本文探讨了教学中对不同类型传感器知识体系的凝练方法，以综合案例的分析对不同学科知识进行交叉应用介绍，希望能够加深学生对理论知识的理解，提高课堂的参与度，锻炼学生综合思考的能力。     

Zero-Sequence Voltage-based Method for Determination and Classification of Unloaded Overhead Line Operating Conditions at the Moment of Energization

Overhead line energization can be performed under normal or faulty conditions. The latter indicates an occurrence of a fault along the line that exists at the moment of energization. This can lead to significant over-voltages that could endanger proper line operation. The issue gets more complex to protective devices when it comes to high-resistance faults, which impair the ability of relays to react promptly and accurately. Consequently, installation of additional detection devices becomes necessary. This paper proposes a novel method that properly detects and classifies line operating conditions at the moment of energization. The method is designed to be useful for utilities and it can be considered as a low-cost, fast, and accurate detection and classification approach, suitable for dealing with both low-resistance and high-resistance faults. Through comprehensive mathematical modeling it was found that both normal and faulty conditions during line energization are accompanied by the zero-sequence voltages of specific characteristics. The differences between zero voltage sequences are reflected in harmonic content, magnitudes of dominant frequencies, and their phase angles in regard to supply voltage. These findings are taken as the method's detection and classification criteria. The validity of the proposed model is verified by simulations and by field measurements.     

断层应力区巷道顶板支护优化设计

1208运巷在过F4断层时,巷道顶板出现破碎、下沉以及局部冒顶现象,不仅降低了巷道掘进效率,而且威胁施工安全。通过分析原巷道支护设计存在的主要问题,对原支护进行优化,提出了“注浆锚棚+纵向桁架锚索”等联合支护措施。通过实际应用效果观察发现,支护设计优化后,加强了顶板稳定性,防止了顶板冒落事故发生。     

Asynchronous adaptive output feedback sliding mode control for Takagi-Sugeno fuzzy Markovian jump systems with actuator faults

In this article, the problem of asynchronous adaptive dynamic output feedback sliding mode control (SMC) for a class of Takagi-Sugeno (T-S) fuzzy Markovian jump systems (MJSs) with actuator faults is investigated. The asynchronous dynamic output feedback control strategy is employed, as the nonsynchronization phenomenon of jump modes exists between the plant and the controller. A novel asynchronous adaptive SMC approach is proposed to solve the synthesis problem for T-S fuzzy MJSs with actuator faults. Sufficient conditions for stochastic asymptotic stability of T-S fuzzy MJSs are given. Under the designed asynchronous adaptive SMC scheme, the effects of actuator faults and external disturbance can be completely compensated and the reachability of sliding surface is ensured. Finally, an example is provided to demonstrate the effectiveness of the proposed design techniques.     

Optimization of waste quail eggshells as biocomposites for polyaniline in ammonia gas detection

A simple, cost-effective, and novel chemical sensor for ammonia (NH₃) gas detection was developed from polyaniline (PANI)/quail eggshell (QES) composites. QES is a natural waste enriched in calcium carbonate. In this work, pure PANI was synthesized from chemical oxidation method and PANI/QES composites were prepared from physical mixing of QES with the synthesized PANI at different mass ratio. A series of complementary techniques including Fourier transform infrared and ultraviolet-visible spectrometers, scanning electron microscope with energy dispersive detection coupled with mapping, thermogravimetric analysis, and X-ray diffractometer were used to characterize the physicochemical and textural properties of the biocomposites. From the results, PANI/QES composite with a mass ratio of 1 exhibited the lowest NH₃ detection limit of 5.24 ppm with a linear correlation coefficient (R²) of close to unity (0.9932) between the signal and NH₃ gas concentration. As a whole, the PANI/QES biocomposites synthesized from this work exhibited excellent selectivity toward NH₃ gas even in the presence of other gas impurities, such as acetone, ethanol, and hexane. For the sensor reusability, the PANI/QES biocomposites can be reused in the application of NH₃ gas detection for at least 4 cycles.