This paper concerns detection method of contact-type failure based on nonlinear wave modulation using self-excited ultrasonic vibration. Linear ultrasonic inspection cannot detect contact-type failure. In vibrating condition, the contact condition of failure fluctuates in synchronization with vibration. In this condition, the amplitude and phase of ultrasonic are modulated by vibration frequency (nonlinear wave modulation). Failure detection method based on nonlinear wave modulation can detect, localize and evaluate contact-type failure. On the other hand, degree of modulation is varied by damping coefficient. The purpose of this study is to propose the failure detection method of contact-type failure which is independent of damping coefficient. Firstly, the concept of nonlinear wave modulation using self-excited ultrasonic vibration is indicated. Secondly, the time response analysis using single-degree-of-freedom model of nonlinear wave modulation is done. As a result, frequency modulation of ultrasonic vibration is occurred by nonlinear wave modulation using self-excited ultrasonic vibration. Finally, the failure index based on frequency modulation is independent of by damping effect.

Crack growth analysis of ferrite by AE (Acoustic Emission) technique was analyzed. In the analysis, fractal dimensions were calculated using image processing and AE energy calculation techniques. According to the analysis of AE energy, the micro crack propagation strength generated in the material was analyzed. In the experiment, ferrite material was used and a tensile experiment was conducted. As a result, crack damage occurred when a proportional relationship was observed in the AE parameter crack volume. By the presented study, it was found that AE nondestructive inspection which evaluated the micro crack propagation strength of material damage by AE energy analysis is possible to evaluate from the proposed image processing technique as well.

We realized Lamb waves generation in transparent polymer materials using non-contact laser-induced shock wave. The generated Lamb waves were measured with a scanning laser Doppler vibrometer. The effectiveness of our method was demonstrated by comparing the measured and calculated propagation phase velocities of the Lamb waves. In this experiment, the polycarbonate plates were used as transparent polymer materials.

In this study, the applicability of the nonlinear piezoelectric impedance modulation technique is preliminarily discussed as a methodology of in-service monitoring of the meshing condition of mating spur gears. In the conventional approaches of the gear condition monitoring, vibration-based methodologies have been intensively studied and already commercialized because of the advantages of their simplicity. It is, however, hard to apply these approaches to the condition monitoring of low-speed gears because they are based on the detection of the impulsive response of the gear induced by a damaged tooth. The goal of this study is to develop more direct and active approach to the condition monitoring of gear teeth meshing. To this end, an ultrasonic elastic wave-based techniques and the concept of smart structures are introduced. In this paper, a piezoelectric wafer actuator is attached to the end face of the target gear, and the electro-mechanical coupling admittance of the piezoelectric actuator is measured to evaluate the sensitivity of the natural frequencies of the gear in the high-frequency range to the rotation angle and the change of the transfer torque under a low-speed rotating condition. It is shown that the piezoelectric current response to the applied high-frequency voltage is modulated in synchronization with the mesh frequency as well as periodicity of the nodal lines of the induced vibration mode.

In this paper, the dependence of the damage indexes used in the nonlinear piezoelectric impedance modulation method proposed by the authors on the amplitude of the pump excitation has been experimentally investigated. It has been confirmed that the radius of the dimensionless stiffness fluctuation, which is evaluated from the instantaneous admittance of the piezoelectric element extracted from the current response of it to the high-frequency probe voltage, shows a linear dependence when the pump amplitude is sufficiently small, but rapidly loses the linearity as the pump becomes larger. A possibility of improved damage metrics based on the dependence of the instantaneous admittance on the pump response has been suggested by applying a rotational coordinate transform on its locus.

In industrial rotary machines, it is indispensable to grasp the torsional natural frequency of the rotor-bearing system in order to avoid resonance. For reciprocating internal combustion engines, it is important to grasp the torsional vibration amplitude because forced torque excitation is inevitable. In this paper, two cases below are picked up as the evaluation case studies torsional vibration using the rotation pulse signals that can be measured with relative ease. One is a case where natural frequency of torsion is evaluated for a rotor of a steam turbine generator and the other is a case where the time and frequency domain amplitude of torsional vibration is evaluated in real-time for a crankshaft of a gas engine generator.

In recent years, there has been an increasing interest in deep learning technique for bearing flaking diagnosis, because it is possible to select vibration features and set diagnostic thresholds without domain knowledge of bearing diagnosis. The authors has proposed previously the CNN-LSTM model trained by using various dataset which would be better generalization performance than that in studies ever reported , i.e., the model might be available for actual rotating machinery, in which vibration feature is affected by type of bearings, various operating conditions and unknown disturbance. In this study, the model was analyzed by Grad-CAM, which was known as a visualization tool for deep learning model for image data, to know how the model detects the flaking. The analysis of Grad-CAM has shown that the periodic impulsive waveforms were detected when the test signal derived were of fault bearings as well expertized engineer will do. Furthermore, it is proved that the extracted feature is still available even though the waveforms were contaminated with white noise. In addition, the analysis also revealed the over-fitting situation of trained model. Therefore, it was concluded that Grad-CAM analysis was able to evaluate the trained deep learning models of bearing vibration diagnosis.

Bearing fault signal plays a crucial role in fault diagnosis accuracy. However, in some conditions, the fault signal is weak and overwhelmed by other noise sources, it is hard to extract fault signal information effectively. In order to solve such problem, this study proposed an automatic bearing fault signal filtering and diagnosis method based on empirical mode decomposition (EMD) and C0 complexity. Firstly, the bearing vibration signals are decomposed into a set of intrinsic mode functions (IMFs) by EMD. Secondly, C0 complexity is proposed to evaluate the fault signal information of IMFs. Thirdly, based on the created IMF discriminate criterion, the IMFs C0 complexity value combining with the normal condition C0 complexity value could automatically select the IMFs which represent the fault signals. Finally, the reconstructed signal based on selected IMFs can filter the noise and be used to diagnose fault accurately. The efficacy of the proposed method is confirmed by applying it to an experimental low-speed bearing.

As for much industry plants, 40 years passed after construction. The rationalization of the inspection for corrosion under insulation(CUI) of the piping is necessary now. There is much piping in the high place, and a large amount of expenses such as appurtenant work cost. We are promoting the development of the “Low Noise Neutron Moisture Meter Robot” and “Compact X-ray System Robot”. This report introduces the development of “Low Noise Neutron Moisture Meter Robot” mainly.

Sometimes high water flow scours bridge piers to reduce safety of bridge. To keep safety, suitable inspection is required. Unfortunately, it is difficult to inspect all bridges because a local government has many bridges and does not have enough inspectors. So, a reliable monitoring system should be developed. In this paper, a new inspection technique estimating amplitude ratio as a index of scour was proposed and tested at a existing bridge. We installed vibration sensors on two piers and checked its difference between two piers. Finally, our experiment showed good correlation between reduction of amplitude ratio and scour.

In this paper, we model the oil film bearings and estimate the fluid-induced instability for the design and the diagnosis of the rotating machinery system. The presented model is implemented in our original rotating machinery library by Modelica. To validate our models, we compared the stabilities of both experiment with a rotor kit and Modelica simulation as a test case.

Although a large amount of data (especially in abnormal conditions) is necessary to predict deterioration of nuclear power plant equipment, it is quite difficult to obtain it. For this reason, simulation is expected to be an effective approach to estimate conditions of equipment instead of data acquisition. In this study, we focused on and built a model for a motor operated valve, since the inspection cost is generally expensive due to the number of the valves used in a nuclear power plant. We analyzed the transient of stem thrust in normal and abnormal conditions. Our analytical results successfully reproduced the difference in the stem thrust during valve moving.

In-plane bending vibration mode in cylindrical shell could be used as a good pipe inspection method. The information acquired from flattening test in the laboratory could also be obtained by non-destructive test if the modal equivalent stiffness of in-plane bending vibration mode could be decomposed from the measured eigen frequency. For this purpose, the modal equivalent mass and stiffness of this mode is investigated using finite element method. First, modal equivalent stiffness is calculated using static analysis of cylindrical shell. Second, eigen frequency is calculated using frequency response analysis. From the modal equivalent stiffness and the eigen frequency, modal equivalent mass is calculated. These calculated parameters are compared with the two dimensional ring approximation theory. Moreover, the formula for estimating modal equivalent stiffness from eigen frequency is derived.

New inspection system is supposed to be introduced in nuclear power plants in Japan. In the corrective action program based on quality management system, sharing of nonconformity information, management of corrective and preventative action are required. For management of the corrective action, relevant data have to be correctly and efficiently recorded and managed. In order to address the new system, we developed a maintenance support tool using augmented reality (AR) technology. This tool helps users identify apparatus or parts to be inspected correctly with self-localization and 3D object recognition technology, and assists users’ recognition and judgment of the object conditions. Some of the functions of the tool are presented.

鉄鋼設備の安定稼働に向けて，状態監視保全を支える設備診断技術の高度化および活用強化に取り組んでいる．特に，設備診断の活用強化については，診断技術の運用(PDCA)サイクルを構築するために，設備診断に関する技術標準の制定から，診断運用・実行管理の仕組み構築まで一貫して進めてきた．本稿では，設備診断PDCAサイクルを，計算機システム上で実行管理する仕組み作りについて，実際の業務を交えて技術紹介を行う．あわせて評価診断の手法についても述べる．