Toshio Toyota

Fuzzy diagnosis and fuzzy navigation for plant inspection and diagnosis robot

Plant inspection and diagnosis robot (IDR) must have the ability to widely monitor the condition of plant machinery with only a few sensors, and to quickly discriminate machine failures. Sound information can be used for monitoring the condition and diagnosing the failure of many machines at the same time, so a sound measuring system is suitable for an IDR. However, when using sound information for condition monitoring and inspection, several problems, such as the effect of noise and a dull sensibility to failure signals, etc., must be solved. In this paper, the method of failure detection and navigation for an IDR is discussed by using sound information and fuzzy logic.<<ETX>>

Intelligent diagnosis method of multi-fault state for plant machinery using wavelet analysis, genetic programming and possibility theory

This paper proposes an intelligent diagnosis method for plant machinery in multi-fault state using wavelet analysis, genetic programming (GP), and possibility theory. The wavelet analysis is used to extract feature spectra of multi-fault state from measured vibration signal for the diagnosis. Excellent symptom parameters for distinguishing fault states are automatically generated by GP. Because the value of symptom parameter calculated to express the feature of the vibration signal fluctuates even if machine state does not change, fuzzy diagnosis is necessary. After obtaining the excellent symptom parameters by GP called GP-SPs, the membership functions of GP-SPs are needed for fuzzy diagnosis. We also discuss the identification method of membership function of symptom parameters using probability theory and possibility theory, and show the inference method for identifying faults types. The methods proposed in this paper are verified by applying them to the diagnosis of rolling bearing in multi-fault state.

Failure detection and diagnosis of rotating machinery by orthogonal expansion of density function of vibration signal

The authors present a new robust failure detection and diagnosis method based on a statistical hypothesis on vibration characteristics of the rotating machines in good condition. The hypothesis is that if the machine is in good condition, its probability density function of the vibration signal follows the normal distribution in time domain. This method based on the hypothesis for characteristics of vibration of good condition can lead to high precision failure diagnosis without any prior knowledge concerning to vibration characteristics corresponding to specific failure to be detected.

Decision-making method of optimum inspection interval for plant maintenance by genetic algorithms

Most of the mathematical methods to decide the time interval of inspection are established by reliability theory, but most of them are difficult to apply to practical plant machinery. In this paper, we propose a decision-making method for the inspection time interval for plant machinery maintenance by a genetic algorithm (GA) when the plant machines are preserved by time-based maintenance (TBM).

Sequential fuzzy diagnosis method and identification method of membership function by probability and possibility theories

When building up a fuzzy diagnosis system for machinery diagnosis, fuzzy relation between failure symptoms and failure categories must be defined for fuzzy inference. However, it is not easy to search out the failure symptoms by which all failure categories can be distinguished perfectly and automatically. In order to resolve the problem, we proposed (1) a new type of symptom parameter function called off-group type of symptom parameter (OGSP); (2) the identification method of the OGSP; (3) the identification method of the membership function of OGSP; (4) the algorithm of sequential fuzzy inference by using the OGSP and its membership function for diagnosis. The efficiency of the above methods has been verified by applying them to the ball bearing diagnosis system.

Failure diagnosis method for machinery in unsteady operating condition by instantaneous power spectrum and genetic programming

The paper proposes a failure diagnosis method for machinery in unsteady operating condition using instantaneous power spectrum (IPS) and genetic programming (GP). The IPS is used to extract feature frequency of each machine state from measured vibration signal for distinguishing failures by the Relative Crossing Information (RCI). Excellent symptom parameters for detecting failures are automatically generated by GP. The method proposed in the paper is verified by applying it to the failure diagnosis of rolling bearing.

Condition monitoring and diagnosis of rotating machinery by Gram-Charlier expansion of vibration signal

Here we present the new robust condition monitoring and diagnosis method based on the statistical hypothesis on vibration characteristics of the rotating machines in good condition. The hypothesis is that if the machine is in good condition, its probability density function of vibration signal follows the normal distribution in time domain. This method can lead to the robust failure diagnosis without any prior knowledge concerning vibration characteristics corresponding to specific failure to be detected.

Plant inspection and diagnosis robot for the detection of a faulty machine part by GA control

The purpose of this study is to develop the basic theories and techniques for a plant inspection and diagnosis robot (IDR). The robot dealt with in this study will work in a large scale unmanned plant or a place with dangerous environment. It has the ability to monitor the condition of plant machinery with only few sensors, and quickly to discriminate machine failures, in order to guarantee both the quality and quantity of production against accident. The paper proposes a method for detecting a faulty part of a plant machine by the robot. A manipulator installed on the IDR is controlled by genetic algorithms (GA). A microphone installed on the manipulator tip is used to detect a failure signal. It is navigated to the nearby front of the faulty part by sound information and GA control. The method has been proved by practical applications.

Intelligent diagnosis method for plant machinery using wavelet transform, genetic programming and possibility theory

This paper proposes an intelligent diagnosis method for plant machinery using wavelet transform (WT) genetic programming (GP) and possibility theory. The WT is used to extract feature spectra of each machine state from measured vibration signal for distinguishing faults. Excellent symptom parameters (SP) for detecting fault states are automatically generated by GP. The membership functions of symptom parameters are established using possibility theory for resolving the ambiguous diagnosis problems. The methods proposed in this paper are verified by applying them to the fault diagnosis of gear equipment.

Extraction of feature spectra by instantaneous power spectrum and automatic generation of symptom parameters by GP for diagnosis of machinery in unsteady operating condition

This paper proposes a failure diagnosis method for plant machinery in unsteady operating conditions using the instantaneous power spectrum (IPS) and genetic programming (GP). The IPS is used to extract the feature spectra of each machine state from the measured vibration signal for distinguishing failures by the relative crossing information (RCI). Excellent symptom parameters for detecting failures are automatically generated by GP. The methods proposed in this paper are verified by applying them to the failure diagnosis of rolling bearings.