Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning
تشخیص خطای دنده نیمه نظارت شده با استفاده از سیگنال لرزش خام بر اساس یادگیری عمیق-2019
In aerospace industry, gears are the most common parts of a mechanical transmission system. Gear pitting faults could cause the transmission system to crash and give rise to safety disaster. It is always a challenging problem to diagnose the gear pitting condition directly through the raw signal of vibration. In this paper, a novel method named augmented deep sparse autoencoder (ADSAE) is proposed. The method can be used to diagnose the gear pitting fault with relatively few raw vibration signal data. This method is mainly based on the theory of pitting fault diagnosis and creatively combines with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear. The effectiveness of the proposed method is validated by experiments of six types of gear pitting conditions. The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy. This method can effectively diagnose different gear pitting conditions and show the obvious trend according to the severity of gear wear faults. The results obtained by the ADSAE method proposed in this paper are compared with those obtained by other common deep learning methods. This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value.
KEYWORDS : Deep learning | Gear pitting diagnosis | Gear teeth | Raw vibration signal | Semi-supervised learning | Sparse autoencoder
Research on deep learning in the field of mechanical equipment fault diagnosis image quality
پژوهش بر روی یادگیری عمیق در زمینه کیفیت تصویر تشخیص خطای تجهیزات مکانیکی-2019
Image quality assessment (IQA) is an indispensable technique in computer vision, which is widely applied in image classification, image clustering. With the development of deep learning, deep neural network (DNN)-based methods have shown impressive performance. Thus, in this paper, we propose a novel method for mechanical equipment fault diagnosis based on IQA. More specifically, we first conduct data acquisition base on our practice. Afterwards, we leverage image processing method for removing noise. Subsequently, we leverage CNN-based method for image classification. Finally, different mechanical equipment images will be grouped into different categories and fault detection can be achieved. Extensive experiments demonstrate the effectiveness and robustness of our method.
Keywords: Deep learning | Mechanical equipment | Equipment maintenance | Image quality
An expert rule-based fault diagnosis strategy for variable refrigerant flow air conditioning systems
یک استراتژی تشخیص عیب مبتنی بر قانون برای سیستم های تهویه مطبوع جریان خنک متغیر -2019
This paper proposed a novel fault diagnosis strategy of the variable refrigerant flow (VRF) system based on expert rules for the first time. The VRF fault diagnosis rules (VFDR) are obtained through the expert knowledge and characteristics of the VRF system. The VFDR includes a total of 22 expert rules, 10 rules for the outdoor unit and other 12 rules for the indoor unit. The proposed VFDR can help maintenance personnel to identify and eliminate VRF faults in time. In addition to obtaining the coefficients of the sensor regression model, the VFDR does not require a training process and is computationally simple, making it easily embedded in the building’s automatic control system to achieve online fault diagnosis. The proposed fault diagnosis strategy is validated with nine faults of the VRF system under cooling mode. These faults contain temperature sensor faults, the system fault and indoor unit faults. The diagnosis correct rate (DCR) is used to evaluate the performance of the VFDR. Through expert rules fault diagnosis strategy, the DCRs of all faults exceed 70% and the overall DCR of all faults is 85.13%. The results show that faults of VRF system are well diagnosed by fault diagnosis strategy based on expert rules.
Keywords: Fault diagnosis | Variable refrigerant flow | Expert rules | Air conditioning | Energy saving
Intelligent fault diagnosis of cooling radiator based on deep learning analysis of infrared thermal images
تشخیص خطای هوشمند رادیاتور خنک کننده بر اساس تجزیه و تحلیل یادگیری عمیق از تصاویر حرارتی مادون قرمز-2019
Detection of faults and intelligent monitoring of equipment operations are essential for modern industries. Cooling radiator condition is one of the factors that affects engine performance. This paper proposes a novel and accurate radiator condition monitoring and intelligent fault detection based on thermal images and using a deep convolutional neural network (CNN) which has a specific configuration to combine the feature extraction and classification steps. The CNN model is constructed from VGG-16 structure that is followed by batch normalization layer, dropout layer, and dense layer. The suggested CNN model directly uses infrared thermal images as input to classify six conditions of the radiator: normal, tubes blockage, coolant leakage, cap failure, loose connections between fins & tubes and fins blockage. Evaluation of the model demonstrates that leads to results better than traditional computational intelligence methods, such as an artificial neural network, and can be employed with high performance and accuracy for fault diagnosis and condition monitoring of the cooling radiator under various working circumstances.
Keywords: Cooling radiator | Fault detection | Thermal image analysis | Deep learning | Convolutional neural network
Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: A deep reinforcement learning approach
تشخیص خطای هوشمند برای ماشین آلات در حال چرخش با استفاده از طبقه بندی حالت سلامت مبتنی بر شبکه Q عمقی: یک روش یادگیری تقویتی عمیق-2019
Fault diagnosis methods for rotating machinery have always been a hot research topic, and artificial intelligencebased approaches have attracted increasing attention from both researchers and engineers. Among those related studies and methods, artificial neural networks, especially deep learning-based methods, are widely used to extract fault features or classify fault features obtained by other signal processing techniques. Although such methods could solve the fault diagnosis problems of rotating machinery, there are still two deficiencies. (1) Unable to establish direct linear or non-linear mapping between raw data and the corresponding fault modes, the performance of such fault diagnosis methods highly depends on the quality of the extracted features. (2) The optimization of neural network architecture and parameters, especially for deep neural networks, requires considerable manual modification and expert experience, which limits the applicability and generalization of such methods. As a remarkable breakthrough in artificial intelligence, AlphaGo, a representative achievement of deep reinforcement learning, provides inspiration and direction for the aforementioned shortcomings. Combining the advantages of deep learning and reinforcement learning, deep reinforcement learning is able to build an end-to-end fault diagnosis architecture that can directly map raw fault data to the corresponding fault modes. Thus, based on deep reinforcement learning, a novel intelligent diagnosis method is proposed that is able to overcome the shortcomings of the aforementioned diagnosis methods. Validation tests of the proposed method are carried out using datasets of two types of rotating machinery, rolling bearings and hydraulic pumps, which contain a large number of measured raw vibration signals under different health states and working conditions. The diagnosis results show that the proposed method is able to obtain intelligent fault diagnosis agents that can mine the relationships between the raw vibration signals and fault modes autonomously and effectively. Considering that the learning process of the proposed method depends only on the replayed memories of the agent and the overall rewards, which represent much weaker feedback than that obtained by the supervised learning-based method, the proposed method is promising in establishing a general fault diagnosis architecture for rotating machinery.
Keywords: Fault diagnosis | Rotating machinery | Deep reinforcement learning | Deep Q-network
Machine learning-based image processing for on-line defect recognition in additive manufacturing
پردازش تصویر مبتنی بر یادگیری ماشین برای تشخیص خطای آنلاین در ساخت مواد افزودنی-2019
A machine learning approach for on-line fault recognition via automatic image processing is developed to timely identify material defects due to process non-conformities in Selective Laser Melting (SLM) of metal powders. In-process images acquired during the layer-by-layer SLM processing are analyzed via a bistream Deep Convolutional Neural Network-based model, and the recognition of SLM defective condition-related pattern is achieved by automated image feature learning and feature fusion. Experimental evaluations confirmed the effectiveness of the machine learning method for on-line detection of defects due to process non-conformities, providing the basis for adaptive SLM process control and part quality assuran
Keywords: Machine learning | Additive manufacturing | Fault recognition
Machine learning-based statistical testing hypothesis for fault detection in photovoltaic systems
فرضیه آزمون آماری مبتنی بر یادگیری ماشین برای تشخیص گسل در سیستم های فتوولتائیک-2019
In this paper, we consider a machine learning approach merged with statistical testing hypothesis for enhanced fault detection performance in photovoltaic (PV) systems. The developed method makes use of a machine learning based Gaussian process regression (GPR) technique as a modeling framework, while a generalized likelihood ratio test (GLRT) chart is applied to detect PV system faults. The developed GPR-based GLRT approach is assessed using simulated and real PV data through monitoring the key PV system variables (current, voltage, and power). The computation time, missed detection rate (MDR), and false alarm rate (FAR) are computed to evaluate the fault detection performance of the proposed approach
Keywords: Fault detection | Photovoltaic (PV) systems | Machine learning | Generalized likelihood ratio test (GLRT) | Gaussian process regression (GPR)
Error analysis and detection procedures for elliptic curve cryptography
روشهای تجزیه و تحلیل خطا و روشهای تشخیص رمزنگاری منحنی بیضوی-2019
In this paper, a fault detection scheme is introduced with the ability to perform with increased protection and reliability of the Elliptic Curve Cryptography (ECC) in realistic environments at a competitive price point. Fall of errors in encrypted/decrypted data delivery with the transmission can create the situation of erroneous data occurring. Without fault detection, in the context of encryption as well as decryption process can create the unprotected system due to random faults, and vulnerable to attack. We are striving to overcome these system weaknesses by introducing the application of nonlinear fault detection codes as it allows safeguarding the elliptic curve point while adding and doubling the operations from the fault attacks. The use of these codes ensures almost perfect error discovery capacity with a reasonably priced upfront cost. The ECC cryptosystem offered a failsafe fault detection scheme using an error detecting code with a proven rate of 99% fault detection coverage.
Keywords: Elliptic Curve Cryptography | Fault attack | Fault detection
A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network
یک رویکرد تشخیص خطا مبتنی بر یادگیری عمیق برای فرآیند شیمیایی با شبکه باور عمیق گسترده-2019
Deep learning networks have been recently utilized for fault detection and diagnosis (FDD) due to its effectiveness in handling industrial process data, which are often with high nonlinearities and strong correlations. However, the valuable information in the raw data may be filtered with the layer-wise feature compression in traditional deep networks. This cannot benefit for the subsequent fine-tuning phase of fault classification. To alleviate this problem, an extended deep belief network (EDBN) is proposed to fully exploit useful information in the raw data, in which raw data is combined with the hidden features as inputs to each extended restricted Boltzmann machine (ERBM) during the pre-training phase. Then, a dynamic EDBN-based fault classifier is constructed to take the dynamic characteristics of process data into consideration. Finally, to test the performance of the proposed method, it is applied to the Tennessee Eastman (TE) process for fault classification. By comparing EDBN and DBN under different network structures, the results show that EDBN has better feature extraction and fault classification performance than traditional DBN.
Keywords: Fault detection and diagnosis | Deep learning | Deep belief network | Extended DBN
Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism
درک و بهبود تشخیص تحمل خطامبتنی بر یادگیری عمیق با مکانیسم توجه-2019
In the recent years, deep learning-based intelligent fault diagnosis methods of rolling bearings have been widely and successfully developed. However, the data-driven method generally remains a “black box”to researchers and there is a gap between the emerging neural network-based methods and the well- established traditional fault diagnosis knowledge. This paper proposes a novel deep learning-based fault diagnosis method for rolling element bearings. Attention mechanism is introduced to assist the deep net- work to locate the informative data segments, extract the discriminative features of inputs, and visualize the learned diagnosis knowledge. Experiments on a popular rolling bearing dataset intuitively show the effectiveness of the proposed method, which is able to provide reliable diagnosis even with very few training data. The experimental results suggest this research offers a promising tool for intelligent fault diagnosis and provides effort in understanding the underlying mechanism of deep neural network.
Keywords: Rolling element bearing | Fault diagnosis |Deep learning | Envelope spectrum | Attention mechanism