Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

Li, Chuan; Sánchez, René–Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

doi:10.3390/s16060895

Cited by 209 publications

(103 citation statements)

References 46 publications

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“…Li et al used a model based on a deep Gaussian-Bernoulli Boltzmann machine (GDBM) exploiting the statistical characteristics of the vibration measurements of rotating machines. The signals of the vibration sensors collected by rotating mechanical systems are represented in the time, frequency and time frequency domains, each of which is then used to produce a set of statistical characteristics [31].…”

Section: Of 17mentioning

confidence: 99%

Fault Diagnosis for UAV Blades Using Artificial Neural Network

2019

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In recent years, unmanned aerial vehicles (UAVs) have been used in several fields including, for example, archaeology, cargo transport, conservation, healthcare, filmmaking, hobbies and recreational use. UAVs are aircraft characterized by the absence of a human pilot on board. The extensive use of these devices has highlighted maintenance problems with regard to the propellers, which represent the source of propulsion of the aircraft. A defect in the propellers of a drone can cause the aircraft to fall to the ground and its consequent destruction, and it also constitutes a safety problem for objects and people that are in the range of action of the aircraft. In this study, the measurements of the noise emitted by a UAV were used to build a classification model to detect unbalanced blades in a UAV propeller. To simulate the fault condition, two strips of paper tape were applied to the upper surface of a blade. The paper tape created a substantial modification of the aerodynamics of the blade, and this modification characterized the noise produced by the blade in its rotation. Then, a model based on artificial neural network algorithms was built to detect unbalanced blades in a UAV propeller. This model showed high accuracy (0.9763), indicating a high number of correct detections and suggests the adoption of this tool to verify the operating conditions of a UAV. The test must be performed indoors; from the measurements of the noise produced by the UAV it is possible to identify an imbalance in the propeller blade.

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Section: Of 17mentioning

confidence: 99%

Fault Diagnosis for UAV Blades Using Artificial Neural Network

2019

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“…Regression trees, K-nearest-neighbor classifier and SVM were adopted as the classifiers, respectively. Li et al [12] used Gaussian-Bernoulli Deep Boltzmann Machine (GDBM) to learn from statistical features of time domain, frequency domain and time-frequency domain. GDBM was also taken as the classifier.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence

Zhang

Peng

et al. 2017

Sensors

103

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Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure the safety of machinery. Conventional fault diagnosis and classification methods usually implement pretreatments to decrease noise and extract some time domain or frequency domain features from raw time series sensor data. Then, some classifiers are utilized to make diagnosis. However, these conventional fault diagnosis approaches suffer from the expertise of feature selection and they do not consider the temporal coherence of time series data. This paper proposes a fault diagnosis model based on Deep Neural Networks (DNN). The model can directly recognize raw time series sensor data without feature selection and signal processing. It also takes advantage of the temporal coherence of the data. Firstly, raw time series training data collected by sensors are used to train the DNN until the cost function of DNN gets the minimal value; Secondly, test data are used to test the classification accuracy of the DNN on local time series data. Finally, fault diagnosis considering temporal coherence with former time series data is implemented. Experimental results show that the classification accuracy of bearing faults can get 100%. The proposed fault diagnosis approach is effective in recognizing the type of bearing faults.

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“…They claimed that their approach gave better results than (then current) peer methods for the detection of faults and fault patterns in gearbox and bearing systems. To tackle this issue, a model for deep statistical feature learning from vibration measurements of rotating machinery was presented by Li et al [97] and the flowchart. Vibration sensor signals were collected from rotating mechanical systems in the time, frequency, and time-frequency domains, each of which was then used to generate a statistical feature set.…”

Section: Gearboxesmentioning

confidence: 99%

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

2018

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This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A diagram of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results.Keywords: artificial intelligence; smart machine tools; learning algorithms; intelligent manufacturing; fault diagnosis and prognosis Brief IntroductionWe believe that a new epoch of the "Industrial Internet of Things (IIoT) plus artificial intelligence (AI)", characterized by big machinery data, data-driven techniques, ubiquitous networks, mass innovation, automatic intelligence, cross-border integration, and shared services, has arrived [1-3]. The fast development and combination of new AI and energy technologies, for materials, bioscience, the Internet, and new-generation information exchange, is a fundamental part of this new epoch. This will, in turn, permit game-changing transformation of models, ecosystems, and means in the light of their application to national security, well-being, and the economy. The main objective is a review and summary of recent achievement in data-based techniques, especially for complicated industrial applications, offering reference for further study from both an academic and practical point of view. Yin et al.[1] describes a brief evolutionary overview of data-based techniques over the last two decades. Recent development of modern industrial applications is presented mainly from the perspectives of monitoring and control. Their methodology, based on process measurements and model-data integrated techniques, will be introduced in the next study. Jeschke et al. [2] developed the core system science needed to enable the development of complex IIoT/manufacturing cyber-physical systems (CPS). Moreover, readers can learn the current state of IIoT and the concept of cybermanufacturing from this book. In 2014, Lund et al. [3] described the central issues contributing to, and characterizing, the worldwide and regional growth of the IoT. Besides, researchers can utilize the trend analysis of IoT their region markets in the future.There are many AI algorithms for machine health monitoring and other machine tool applications: The second-order recurrent neural networks (RNN) method for the learning and extraction of finite

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Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

Cited by 209 publications

References 46 publications

Fault Diagnosis for UAV Blades Using Artificial Neural Network

Fault Diagnosis for UAV Blades Using Artificial Neural Network

Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

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