A review on machinery diagnostics and prognostics implementing condition-based maintenance

Rotating machinery prognostics: State of the art, challenges and opportunities

Cost-effective and reliable damage detection is critical for the utilization of composite materials. This paper presents part of an experimental and analytical survey of candidate methods for the in situ detection of damage in composite materials. The experimental results are presented for the application of modal analysis techniques applied to graphite/epoxy specimens containing representative damage modes. Changes in natural frequencies and modes were found using a laser vibrometer, and 2-D finite element models were created for comparison with the experimental results. The models accurately predicted the response of the specimens at low frequencies, but coalescence of higher frequency modes makes mode-dependant damage detection difficult for structural applications. The frequency response method was found to be reliable for detecting even small amounts of damage in a simple composite structure, however the potentially important information about damage type, size, location and orientation were lost using this method since several combinations of these variables can yield identical response signatures.

Damage detection in composite materials using frequency response methods

Cost-effective and reliable damage detection is critical for the utilization of composite materials This thesis presents the conclusions of an analytical and experimental survey of candidate methods for in-situ damage detection in composite materials Finite element results are presented for the application of modal analysis and Lamb wave techniques to quasi-isotropic graphite/epoxy test specimens containing representative damage These results were then verified experimentally by using piezoelectric patches as actuators and sensors for both sets of experiments The passive modal analysis method was reliable for detecting small amounts of global damage in a simple composite structures By comparison, the active Lamb wave method was sensitive to all types of local damage present between the sensor and actuator, provided useful information about damage presence and severity, and presents the possibility of estimating damage type and location Analogous experiments were also performed for more complex builtup structures such as sandwich beams, stiffened plates and composite cylinders These techniques have proven suitable for structural health monitoring applications since they can be applied with low power conformable sensors and can provide useful information about the state of a structure during operation Piezoelectric patches could also be used as multipurpose sensors to test using a variety of methods such as modal analysis, Lamb wave, acoustic emission and strain based methods simultaneously by altering driving frequencies and sampling rates Guidelines and recommendations drawn from this research are presented to assist in the design of a structural health monitoring system for a vehicle, and provides a detailed example of a SHM system architecture These systems will be an important component in future designs of air and spacecraft to increase the feasibility of their missions Thesis Supervisor: S Mark Spearing Title: Associate Professor of Aeronautics and Astronautics

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Piezoelectric-based in-situ damage detection of composite materials for structural health monitoring systems

Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.

/pdf/fault-diagnosis-for-rotating-machinery-a-method-based-on-4jvufzdjlj.pdf

Fault Diagnosis for Rotating Machinery: A Method based on Image Processing

Fault Diagnostics Using Statistical Change Detection in the Bispectral Domain

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Fault Diagnostics Using Statistical Change Detection in the Bispectral Domain