Mechanical fault diagnosis based on digital image processing technology

Modern mechanical equipment continues to generate high productivity, at the same time, it has brought many new challenges and new problems. It is of great importance to detect and diagnose mechanical fault diagnosis in time and then provide alarm messages. Therefore, in this paper, we aim to propose a novel mechanical fault diagnosis with the digital image processing technology. Firstly, overall structure of mechanical equipment fault dynamic motion detection system is illustrated and the infrared image segmentation is the key part this system. Secondly, we convert the infrared image segmentation problem to a cluster problem and then, provide a novel immune neural network cluster algorithm. Finally, a representative region for a specific machinery condition is selected according to the largest dispersion degree criterion. Experimental results demonstrate that the proposed algorithm can detect mechanical faults with high accuracy.

Recent Progress on Mechanical Condition Monitoring and Fault Diagnosis

The history of Chinese ceramics has become the representative of southern ceramics with its unique celadon carving, black glaze mining, painting, printing and other decorative styles. Minnan architectural ceramics are fresh carriers, samples and memories of Chinese cultural symbols and aesthetic habits. Based on the digital image processing technology, this paper analyzes the decorative patterns of southern Fujian architectural ceramics, introduces the digital image calibration principle and sampling quantification method used in the decorative patterns of southern Fujian architectural ceramics, and then uses image segmentation and binarization analysis, color digital image processing and analysis technology to discover the abstract beauty of Minnan ceramic ornamentation.

Various mechanical equipment play a crucial role, and their health or status may affect efficiency and safety seriously. Spectrum analysis of the corresponding signal has been widely used to diagnose the fault in the past decades. The diagnosis method based on spectrum analysis technology covers almost all aspects of mechanical fault diagnosis. However, there is a lack of review of diagnostic methods of spectrum analysis technologies in the field of mechanical equipment fault diagnosis. In order to fill this gap, this paper reviews the spectrum analysis technology in mechanical equipment diagnosis in detail. First of all, in order to let the researchers who are in contact with spectrum analysis technology for the first time quickly understand this field, the principles of spectrum are systematically sorted out, including spectrum, cepstrum, energy spectrum, power spectrum, higher-order spectrum, Hilbert spectrum, marginal spectrum, envelope spectrum, singular spectrum and so on. Furthermore, the characteristics of corresponding spectrum analysis technologies are summarized, and their advantages and disadvantages are analyzed and compared. High-quality references in recent ten years are cited for illustration to enhance persuasiveness. Finally, the prospect of spectrum analysis technology is summarized, and the future development trend of spectrum analysis technology is pointed out. It is believed that the joint diagnosis of fault severity, variable speed fault diagnosis, combined with deep learning and multiple spectrum analysis technologies should be given more attention in the future. This paper is expected to provide a comprehensive overview of mechanical fault diagnosis based on spectrum analysis theory, and help to develop corresponding spectrum analysis technologies in practical engineering.

Coupled neurons with multi-objective optimization benefit incipient fault identification of machinery

In an intelligent manufacturing context, the smooth operations of mechanical equipment in the production process of enterprises and timely fault diagnosis during operations have become increasingly important. However, the effect of traditional fault diagnosis depends on the feature extraction quality and experts’ empirical knowledge, which is inefficient and costly, and cannot match the needs of mechanical equipment fault diagnosis in intelligent manufacturing. The TSK fuzzy system has a strong approximation capability and the ability to interpret expert knowledge. The broad learning system (BLS) has strong feature extraction and fast computation capabilities. In this paper, we present a new model—the TSK fuzzy broad learning system (TSK-BLS). The model integrates the advantages of the BLS and the fuzzy system at the same time, which can be calculated quickly and accurately by pseudo-inverse and symmetry methods. On the other hand, the model is an embedded model-building mechanism, which extends the application scope of BLS theory. The model was tested on a bearing fault data set, provided by Case Western Reserve University, and the model’s fault diagnosis accuracy was as high as 0.9967. The results were compared with those of the convolutional neural network (CNN) and the BLS models, whose fault diagnosis accuracies are 0.6833 and 0.9133, respectively. Comparison showed that the proposed fault diagnosis model—TSK-BLS, achieved significant improvements.

The difficulty of feature extraction and the small sample size are two challenges in the field of mechanical fault diagnosis for a long time. Here we propose an intelligent mechanical fault diagnosis method for scenario with small sample datasets. This method can not only diagnose bearing faults but also gear faults, and has strong generalization performance. We use convolutional neural network to realize automatic feature extraction. Through sliding window scanning, one sample set is expanded to three sub-sample sets with different scales to meet the needs of deep learning training. Three convolutional networks are used to extract the features of the subsets respectively to ensure that their useful features are fully extracted. After feature extraction, the feature is reconstructed through feature splicing. Because of the unique advantages of SVM in dealing with small sample sets, we use SVM to classify the reconstructed features. We use the bearing data set collected by Case Western Reserve University in the United States, the bearing fault data set collected by Xi'an Jiaotong University in China, and the gearbox fault data collected by the University of Connecticut in the United States to conduct experiments. The experimental results show that the accuracy of training, validation and testing of the proposed method on the three data sets all reach 100%. This proves that our method can not only tackle the two challenges, but also has high fault diagnosis accuracy and strong generalization performance. It is hoped that our proposed method can contribute to the development of mechanical fault diagnosis.

A new mechanical fault diagnosis method for high-voltage isolation switches in power grids is proposed, which often leads to mechanical faults such as bearing jamming and inadequate action, resulting in inaccurate fault signal characteristic values and poor diagnostic results. Based on the characteristic values of vibration signals when mechanical failures occur, we draw a torque angle detection curve and compare it with the curve changes under normal operating conditions to achieve an accurate diagnosis of mechanical failures. The experimental results demonstrate that the mechanical fault diagnosis results of the designed method are consistent with reality, indicating that this method can effectively detect mechanical faults in high-voltage isolation switches in the power grid and has good diagnostic performance.

Under the premise of the continuous development of modern industrial automation, modern mechanical equipment is constantly toward automation, modernization, continuous process of development. Those traditional fault solutions have been unable to adapt to the development channels of modern mechanical equipment. It brings many new problems to the fault diagnosis system. In addition, intelligent technology is a significant sign of the development direction and strategy of emerging industries. In particular, it has significant advantages in solving circuit integration, cost budgeting and circuit diagnosis. It brings great convenience to the traditional mechanical fault diagnosis technology. This article is based on the traditional mechanical common faults, diagnostic methods and related concepts and it also describes some intelligent techniques that have been applied to solve the problems. The application of artificial intelligence in mechanical fault diagnosis is discussed.

The development of condition monitoring and fault diagnosis can improve the security and reliability for CNC (computer numerical control) machines, industrial robots and other key manufacturing equipment. Compared with traditional vibration analysis methods, motor drive system, the actuator in mechatronic system, can be served as the self-sensing equipment and provides a mechanical fault diagnosis approach without external sensors. This paper focuses on the application of motor drive in mechanical fault diagnosis. Firstly, the mechanical fault diagnosis methods rely on measuring, observing and identifying information from motor drive are summarized. Then, based on mechatronic characteristics and industrial applications, some key issues and potential solutions for diagnosis with self-sensing motor drive system are proposed. Finally, experiment is conducted in practical application to verify the effectiveness of self-sensing signals on mechanical fault detection and location.

To address the issues of single-structured feature input channels, insufficient feature learning capabilities in noisy environments, and large model parameter sizes in intelligent diagnostic models for mechanical equipment, a lightweight and efficient multimodal feature fusion convolutional neural network (LEMFN) method is proposed. Compared with existing models, LEMFN captures rich fault features at multiple scales by combining time-domain and frequency-domain signals, thereby enhancing the model's robustness to noise and improving data adaptability under varying operating conditions. Additionally, the convolutional block attention module (CBAM) and random overlapping sampling technology (ROST) are introduced, and through a feature fusion strategy, the accurate diagnosis of mechanical equipment faults is achieved. Experimental results demonstrate that the proposed method not only possesses high diagnostic accuracy and rapid convergence but also exhibits strong robustness in noisy environments. Finally, a graphical user interface (GUI)-based mechanical equipment fault detection system was developed to promote the practical application of intelligent fault diagnosis in mechanical equipment.

The quality of optics' surface is of an important character which has critical influence in optical system. There are many ways to detect surface quality, but interferometry is considered as an effective technology. Interfere fringes which obtained from our experiment based on equal thickness interference were processed with digital image processing (DIP) technology in this paper. Image smoothing, fringes thinning, fringes' space measurement and P-V value measurement were done in this processing, Which can give optics' surface information automatically and accurately. We measured some optics whose apertures are not more than 30mm, and the result is satisfactory. Our method will be widely used in industrial inspection, especially in optical works in the future.

High voltage circuit breaker is a critical equipment of power system. It is very important to ensure the circuit breaker to operate in a normal state. According to statistics, most defect and fault of high voltage circuit breaker is caused by mechanical faults. In this research, the sound and current signals were collected in the simulation experiment of typical mechanical faults, namely iron core jam, two kinds of tripping mechanism faults, and spring fatigue. Then the signals were down sampled, flipped and stacked to fit deep learning model. A convolution neural network (CNN) model consisting eight layers was developed to extract features and categorize faults from the pre-processed signals. The results indicate that the mechanical fault diagnosis accuracy rate is up to 94%, higher than conventional methods using sound or current signal.

Mechanical faults present a large portion of induction motor failures, if left undetected, it can lead to partial or total breakdown of the machine. This paper propose a scheme to detect and diagnose mechanical faults in an induction motor by the AR model coefficients of the Torque signal. First, the torque signal obtained from experiment in different conditions: healthy condition, motor with dynamic eccentricity fault, and motor with misalignment fault are normalized to exclude the load effect. Then the AR model coefficients are extracted as features to reduce the dimension data while keeping the effective information. Finally, the Self Organizing Map neural network is used for classification of the different conditions. The experimental results show the effectiveness of the proposed method, were both eccentricity and misalignment faults might be easily detected and discriminated from each other.

This work investigates digital image processing technology and presents a super-resolution image reconstruction approach to increase the logic of urban and rural planning spatial layout. Furthermore, this work integrates GIS technology with super-resolution image reconstruction techniques to create a digital image processing and GIS-based urban and rural spatial layout design model. In addition, this paper mathematically expresses the objects and scenes that need to be simulated as a collection of three-dimensional graphic objects stored in the computer. The platform is constructed with a three-tier system of B/S architecture, and the logical structure of the entire system is composed of the client, application server, database server, and other functional servers. Furthermore, this paper constructs the functional structure of urban and rural spatial layout planning model based on digital image processing and GIS technology and verifies the model in this paper through experimental research. The experimental statistical results verify the reasonableness of the model in this paper.