As diagnosis methods for bearings, well-known are those which use acceleration or eddy-current sensors. The disadvantages by the use of the sensors are that they have to be embedded into the casing adding to that they cannot be always applied to the objects. The paper proposes a signal propagation model based diagnosis method using an ultrasonic sensor and illustrates the effectiveness of the method by simulation data.
In case of a flood, earth and sand at bottoms of bridge piers are sometimes washed out and thus yielding unstableness of the bridge piers. Therefore, constant monitoring of bridge piers is of great importance in keeping the safety operation of trains. The paper proposes a simplified monitoring system which uses an acceleration pick-up and analyzes the sensor output (vibration signal) of the bridge piers generated with a light hammer. Finally, the validity of the method is demonstrated with an experiment.
The newest trend on vibration and sound measurement and analysis is stated, and a personal computer base measurement system for general use made by the authors is presented in this report. This system is flexible, easy to use and possible to customize freely according to user's requirement. The outline of system specification and composition is introduced. Main functions of this system are explained. Some examples of application are shown as display pictures.
In addition to the conventional tendency management and failure cause diagnosis, in diagnosis, the measure of the viewpoint of optimum asset-management of performance recovery of the life extension of facilities by CBM and facilities, reduction of energy cost, etc. has come to think as important. It is asked for concentration of knowledge, and the advancement of analysis with the increase in efficiency of the state monitoring, and diagnosis and evaluation of facilities in such a background. The structure of package diagnosis which utilized IT is under employment, and the contents and an example are introduced as structure which realizes these.
In order to monitor the condition of rotating machinery used in power plants or factories, many rotating machinery diagnosis systems have been developed. However, the lifetime prediction capabilities of these diagnosis systems were not enough to determine the appropriate maintenance timing based on the machinery condition. In this paper, to extend the maintenance period of rotating machinery, we have developed a new lifetime prediction method for rolling element bearing that can estimate the remaining life at an earlier stage than conventional methods. In the developing of the lifetime prediction method, we have considered the fatigue mode of rolling element bearings, and we have constructed a life prediction algorithm using a general acceleration sensor for low cost. Developing this method, we can predict the remaining life of rolling element bearings at an early stage, and it can be expected to make maintenance more efficient.
The validity of the cylindrical guide wave is demonstrated for flaw and corrosion detection of pipelines. Using circumferential guided waves, we evaluate precisely inner and outer corrosion depth of pipes. We can identify the location of the corrosion by time-of-flight and the wall thickness by the amplitudes of the guided waves. This method is very efficient because the corrosion at any point on the cicumference is detected by single measurement.
This paper concerns a continuous structural health monitoring method to detect and track evolution of damages, which may cause changes in structural dynamics. A short-time Fourier transform-based simple scheme is developed to extract the time-varying mode shapes and the mode curvatures from ambient vibration data measured at multiple points. Numerical experiments show that the proposed monitoring method is capable to identify the changes in mode curvatures, which can indicate the location of the damages as well as time when they occur.
This paper concerns a preliminary study of classifying the abnormal tension signals observed in the draw-texturing machines, which correspond to various types of abnormal events. The wavelet transform with inhomogeneous shift sampling is used to extract the feature vectors from the tension signals. Then, the self-organizing map (SOM) algorithm is performed on the feature vectors to carry out the unsupervised classification of the signals. The results agree with the classification by skilled engineers that is based on their experience.
Life styles based on mass consumption and mass abandonment, which have been made possible by mass production, need to be rethought from the viewpoints of global environmental problems and future shortages of fossil energy resources. For power equiment and power systems, the life extension of power equipment using various diagnosis techniques is an important subject. The larger in size systems become, the more serious the damage to them becomes. Therefore, more sophisticated diagnosis technologies are required. In this paper, an estimation method of vibrations and forces inside gas insulated switchgear, which are caused by an abnormal condition was discussed. The values and locations of inside forces were estimated by solving an inverse problem of the vibrations using calculations and experiments. In the calculations, up to 50 force locations were well estimated. In experiments, up to 6 force locations were well estimated. New method which can estimate the location of inside forces precisely are proposed.
This paper deals with a failure diagnosis and an evaluation of dynamic characteristics in gear sets. The Wavelet Transform (WT) is a method for the time-frequency analysis. The dynamic characteristics of both steel and sintered gears were measured using a power circulating gear testing machine and were analyzed in a time-frequency domain by the continuos and discrete WTs. Furthermore, the health monitoring of gear sets was carried out and the tooth surface failure by pitting was diagnosed using the WTs. The vibration acceleration of the gear box could be divided to two different behaviors above and below the tooth mesh frequency. A failed tooth could be found out by the change of WT intensity of the vibration acceleration. It could be said that the continuous and discrete wavelet transforms are a useful method for diagnosing the tooth failure and for evaluating the gear dynamics.
To detect a damage of the gear is strongly requested for the reliable operation of transmission systems. Although many methods based on the vibration analysis of the gearbox were proposed, it remained the limitation in the detection of the location and the size of the damage. In this paper, we developed the method based on the estimation of the rotational speed of the gear to detect the location and the size of wear of the tooth of the gear. Our experimental result showed the good performances in precise estimation of tooth wear.
In this paper, a jerk-dot sensor, which measures the second derivative of the acceleration, is developed. Its capability of detecting the local damage in the structural members is investigated through low-cycle fatigue tests, which prove its significant sensitivity to the abnormal responses due to the development of macroscopic damages. Further fracture tests are carried out to obtain the correlation between the measured jerk-dot and the crack length, which suggests that this sensor could provide an early alert before the crack grows up to the fatal stage.
The major causes of material aging of storage tanks and vessels is thickness reduction of plates due to unexpected corrosion. And it is extremely difficult to detect such defects or analyze corrosive trend of the whole plates by other inspection systems. On the other hand, it is asked for curtailment of maintenance expense. Such a background is taken into consideration. We were inquired about the method of grasping the status of the background of tank bottom, and shell and vessels' shell and quantitatively with high precision. We have succeeded in development of inspection equipment and system. An advanced High-speed Inspection System has been developed in order to achieve an accurate inspection for corrosive conditions of whole plates of storage tanks and pressure vessels in a shorter period and at a lower cost. The exact identification of the corroded portions of the plates by this system will enable us to evaluate metal loss and to make appropriate partial repair of the defective, which contribute to extreme cost reduction for repair of defective plates comparing with replacing of the whole plates as done in the past.
The concept of the Mutually Excluding Relations is presented in the paper in order to evaluate the system performance and to discover any abnormality in its operation. For such purpose a cause-effect relation dynamic models are used for revealing the existing relationships between the pairs of parameters in the preliminary designed observation set of parameters. Two Identification schemes are presented, namely for off-line and for real-time identification, based on the Least Squares Estimation and on the Widrow-Hoff learning rule. They are used to create the normal operation model and the current operation model of the system. The degree of discrepancy between these models during the operation of the system is used as a measure for detecting the abnormality of its operation. Computer simulations have been performed that demonstrate in a pictorial way the main idea and its applicability for real-time monitoring, diagnosis and maintenance.
Inspection for machinery industrial products is usually done by a man trusting his sense. Automation of inspection has been desired to overcome the differences by individuals and the shortage of such skilled workers. This paper describes the characteristic extraction method of four abnormal engine sounds arising from "Large clearance between cam and valve lifter" and others caused by typical engine assembly error, in order to develop engine sound diagnostic system of automotive on mass inspection line. The sound data for both normal and abnormal engine were measured by changing engine revolutions and measuring positions. To extract the features of abnormal engine sound, Linear Prediction Coefficient (LPC) is calculated after suitable filtering at adequate engine revolutions. As a result, we have found the characteristic difference between normal and abnormal engine sound. Detection of abnormal engines has become easier by compressing sound data into LPC to find their characteristic.