Fault Diagnosis of Air-conditioning System Using CMAC Neural Network Approach

Hung, Chin-Pao; Wang, Mang-Hui

doi:10.1007/978-1-4471-3744-3_1

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…However, the needed memory size is our major concern in real application. How to develop new fired memory addresses coding technology [18], to obtain optimal memory size for specified resolution, and to implement the diagnosis scheme by chip design are our future work and understudying.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of incipient faults in power transformers using CMAC neural network approach

Hung

Wang

2004

Electric Power Systems Research

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Dissolved gas analysis (DGA) is one of the most useful techniques to detect the incipient faults of power transformer. However, the identification of the faulted location by the traditional method is not always an easy task due to the variability of gas data and operational natures. In this paper, a novel cerebellar model articulation controller (CMAC) neural network (NN) method is presented for the fault diagnosis of power transformers. By introducing the IEC standard 599 to generate the training data, and using the characteristic of self-learning and generalization, like the cerebellum of human being, a CMAC NN fault diagnosis scheme enables a powerful, straightforward, and efficient fault diagnosis. With application of this scheme to published transformers data, the diagnoses demonstrate the new scheme with high accuracy and high noise rejection ability. Moreover, the results also proved the ability of multiple incipient faults detection.

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Section: Discussionmentioning

confidence: 99%

Diagnosis of incipient faults in power transformers using CMAC neural network approach

Hung

Wang

2004

Electric Power Systems Research

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“…In a table look-up fashion, the CMAC produced a vector output in response to state vector input. Since the feature of similar inputs will produce similar outputs, it obtained many successful applications on pattern recognition such as fault diagnosis [10] or image pattern [8]. Extending our previous researches, this work develops a CMAC based object recognition scheme and applies the recognized results to derive the position feedback signal.…”

Section: The Cmac Based Object Recognition Systemmentioning

confidence: 95%

Visual Servo Tracking Control for Slider Robot Using CMAC Neural Network Recognition Approach

Hung

Hsieh

2013

AMM

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This paper describes an image-based visual servo tracking control scheme using CMAC neural network as object recognition feedback methodology. A web camera based image capture system is mounted on the slider robot to capture the desired object and a CMAC (Cerebellar Model Articulation Controller) based object recognition scheme is developed to recognize the captured object image. Comparing the relative location of the recognized object and the web camera, then the position feedback signal can be obtained. Using the feedback signal, a PID (Proportional-Integral-Derivative) controller is designed to track the desired object by moving a single-axis slider robot, such that the captured object image located on the center of the captured frame always.

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Diagnosis of Air-Conditioner by Using Its Dynamic Property

Kawai

Kushizaki²

2014

Lecture Notes in Mechanical Engineering

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Fault Diagnosis of Air-conditioning System Using CMAC Neural Network Approach

Cited by 5 publications

References 7 publications

Diagnosis of incipient faults in power transformers using CMAC neural network approach

Diagnosis of incipient faults in power transformers using CMAC neural network approach

Visual Servo Tracking Control for Slider Robot Using CMAC Neural Network Recognition Approach

Diagnosis of Air-Conditioner by Using Its Dynamic Property

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