scholarly journals Early Fault Diagnosis Method for Batch Process Based on Local Time Window Standardization and Trend Analysis

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8075
Author(s):  
Yuman Yao ◽  
Yiyang Dai ◽  
Wenjia Luo
Keyword(s):  
Fault Diagnosis ◽  
Trend Analysis ◽  
Time Window ◽  
Economic Value ◽  
Batch Process ◽  
Sensor Data ◽  
Batch Processes ◽  
Diagnosis Method ◽  
Available Information ◽  
Fault Information

The products of a batch process have high economic value. Meanwhile, a batch process involves complex chemicals and equipment. The variability of its operation leads to a high failure rate. Therefore, early fault diagnosis of batch processes is of great significance. Usually, the available information of the sensor data in batch processing is obscured by its noise. The multistage variation of data results in poor diagnostic performance. This paper constructed a standardized method to enlarge fault information as well as a batch fault diagnosis method based on trend analysis. First, an adaptive standardization based on the time window was created; second, utilizing quadratic fitting, we extracted a data trend under the window; third, a new trend recognition method based on the Euclidean distance calculation principle was composed. The method was verified in penicillin fermentation. We constructed two test datasets: one based on an existing batch, and one based on an unknown batch. The average diagnostic rate of each group was 100% and 87.5%; the mean diagnosis time was the same; 0.2083 h. Compared with traditional fault diagnosis methods, this algorithm has better fault diagnosis ability and feature extraction ability.

scholarly journals Fault Diagnosis of Brake Train Based on Multi-Sensor Data Fusion

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4370
Author(s):  
Yongze Jin ◽  
Guo Xie ◽  
Yankai Li ◽  
Xiaohui Zhang ◽  
Ning Han ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Expectation Maximization ◽  
Monitoring Data ◽  
Sensor Data ◽  
Operating Environment ◽  
Mass Model ◽  
Multi Sensor Data Fusion ◽  
Diagnosis Method ◽  
Single Mass ◽  
Parameter Identifications

In this paper, a fault diagnosis method is proposed based on multi-sensor fusion information for a single fault and composite fault of train braking systems. Firstly, the single mass model of the train brake is established based on operating environment. Then, the pre-allocation and linear-weighted summation criterion are proposed to fuse the monitoring data. Finally, based on the improved expectation maximization, the braking modes and braking parameters are identified, and the braking faults are diagnosed in real time. The simulation results show that the braking parameters of systems can be effectively identified, and the braking faults can be diagnosed accurately based on the identification results. Even if the monitoring data are missing or abnormal, compared with the maximum fusion, the accuracies of parameter identifications and fault diagnoses can still meet the needs of the actual systems, and the effectiveness and robustness of the method can be verified.

Handling Data Insufficiency Problem in Diagnosing Batch Processes

2003 ◽  
Vol 10 (02) ◽  
pp. 171-184
Author(s):  
Hyun-Woo Cho ◽  
Kwang-Jae Kim
Keyword(s):  
Batch Process ◽  
Batch Processes ◽  
Fisher Discriminant Analysis ◽  
Data Set ◽  
Discriminant Model ◽  
Fisher Discriminant ◽  
Industrial Batch ◽  
Reliable Diagnosis ◽  
Diagnosis Method

To ensure safety of a batch process and quality of its final product, one needs to quickly identify an assignable cause of a fault. To solve the diagnosis problem of a batch process, Cho and Kim6 proposed a new statistical diagnosis method based on Fisher discriminant analysis (FDA). They showed satisfactory diagnosis performance on industrial batch processes. However, the diagnosis method of Cho and Kim6 has a major limitation: it does not work when the fault data available for building the discriminant model are insufficient. In this work, we propose a method to handle the insufficiency of the fault data in diagnosing batch processes. The diagnosis performance of the proposed method is demonstrated using a data set from a PVC batch process. The proposed method is shown to be able to handle the data insufficiency problem, and yield reliable diagnosis performance.

Research on Equipment Fault Diagnosis Method Based on Multi-Sensor Data Fusion

2012 ◽  
Vol 466-467 ◽  
pp. 1222-1226
Author(s):  
Bin Ma ◽  
Lin Chong Hao ◽  
Wan Jiang Zhang ◽  
Jing Dai ◽  
Zhong Hua Han
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Diagnostic Accuracy ◽  
Evidence Theory ◽  
Experimental Results ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Diagnosis Method ◽  
Single Sensor

In this paper, we presented an equipment fault diagnosis method based on multi-sensor data fusion, in order to solve the problems such as uncertainty, imprecision and low reliability caused by using a single sensor to diagnose the equipment faults. We used a variety of sensors to collect the data for diagnosed objects and fused the data by using D-S evidence theory, according to the change of confidence and uncertainty, diagnosed whether the faults happened. Experimental results show that, the D-S evidence theory algorithm can reduce the uncertainty of the results of fault diagnosis, improved diagnostic accuracy and reliability, and compared with the fault diagnosis using a single sensor, this method has a better effect.

scholarly journals Fault diagnosis of a planetary gearbox based on a local bi-spectrum and a convolutional neural network

2021 ◽  
Author(s):  
Lingli Jiang ◽  
LI Shuhui ◽  
LI Xuejun ◽  
Jiale Lei ◽  
YANG Dalian
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Vibration Signals ◽  
Planetary Gearbox ◽  
Gaussian Colored Noise ◽  
Diagnosis Method ◽  
Non Gaussian ◽  
Fault Information ◽  
Fault Characteristic

Abstract The vibration signals of a planetary gearbox have the characteristics of strong background noise and instability and are non-Gaussian. Bi-spectrums can suppress Gaussian colored noise and are suitable for vibration signal processing of planetary gearboxes. In the traditional fault diagnosis methods based on bi-spectrums, the fault characteristic frequency amplitudes of bi-spectrum or bi-spectrum slices, or the further quantitative calculations of fault characteristic values, are generally used as the basis of fault diagnosis processes. It has been found that bi-spectrum images can directly characterize the faults of the planetary gearboxes. Convolutional neural networks (CNNs) have been used in mechanical fault diagnoses in recent years. One-dimensional original signals are converted into two-dimensional images as CNN input, which is an effective method for mechanical fault diagnoses. At the present time, there has not been any relevant research conducted using bi-spectral images as CNN input. In this study, a fault diagnosis method based on local bi-spectrum and CNN was proposed. A bi-spectral analysis of the vibration signals of the planetary gearbox was first carried out in order to reveal the fault information while retaining the non-Gaussian information. Then, according to the bi-spectrum symmetry, local images containing the entire domain information were taken as the input of the CNN, which reduced the redundancy of the fault information. Then, in order to improve the diagnostic accuracy of the CNN, the key parameters of CNN architecture were optimized. Finally, a CNN diagnosis model was built to realize the classification diagnoses of different fault positions and different fault degrees of planetary gearboxes. This study’s comparison of the diagnosis results of the full bi-spectrum+CNN, local bi-spectrum+SVM, original vibration signal+CNN, and local bi-spectrum+BP neural networks showed that the method proposed in this study had achieved both accuracy and rapidity in the fault diagnoses of planetary gearboxes.

Fault Diagnosis Technology Research on Radar Equipment Based on Sensor Data Validation and Detection Fusion

2013 ◽  
Vol 347-350 ◽  
pp. 387-391
Author(s):  
Wen Hua Hu ◽  
Bao Feng Guo ◽  
Dong Fang Xue
Keyword(s):  
Fault Diagnosis ◽  
Diagnostic System ◽  
Sensor Data ◽  
Data Validation ◽  
Characteristic Parameters ◽  
Proposed Model ◽  
Diagnosis Rate ◽  
Diagnosis Method ◽  
Fault Diagnosis System ◽  
Fault Characteristic

Aiming at many fault characteristic parameters of modern radar and the problem that general diagnosis method is difficult to locate fault accurately. The dynamic mathematical model of radar equipment fault diagnosis system is built. The uncertainty theoretic analysis of fault diagnosis result is studied from two aspects, data processing channel and fault model. This paper combines sensor data validation with detection fusion technology, and then puts forward the fault diagnosis technology based on sensor data validation and detection fusion. The structure of the diagnostic system model is given. Case analysis proves that the proposed model and method has a significant role in improving detection performance, reducing the Bayes risk and improve fault diagnosis rate.

scholarly journals Sensor Data-Driven Bearing Fault Diagnosis Based on Deep Convolutional Neural Networks and S-Transform

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2750 ◽  
Author(s):  
Guoqiang Li ◽  
Chao Deng ◽  
Jun Wu ◽  
Xuebing Xu ◽  
Xinyu Shao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Convolutional Neural Networks ◽  
Data Driven ◽  
Sensor Data ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
S Transform ◽  
Diagnosis Method

Accurate and timely bearing fault diagnosis is crucial to decrease the probability of unexpected failures of rotating machinery and improve the efficiency of its scheduled maintenance. Since convolutional neural networks (CNN) have poor feature extraction capability for sensor data with 1D format, CNN combined with signal processing algorithm is often adopted for fault diagnosis. This increases manual conversion work and expertise dependence while reducing the feasibility and robustness of the corresponding fault diagnosis method. In this paper, a novel sensor data-driven fault diagnosis method is proposed by fusing S-transform (ST) algorithm and CNN, namely ST-CNN. First of all, a ST layer is designed based on S-transform algorithm. In the ST layer, sensor data is automatically converted into 2D time-frequency matrix without manual conversion work. Then, a new ST-CNN model is constructed, and the time-frequency coefficient matrixes are inputted into the constructed ST-CNN model. After the training process of the ST-CNN model is completed, the classification layer such as softmax performs the fault diagnosis. Finally, the diagnosis performance of the proposed method is evaluated by using two public available datasets of bearings. The experimental results show that the proposed method performs the higher and more robust diagnosis performance than other existing methods.

Research on Intelligent Fault Diagnosis Method Based on Rough Set Theory and Fuzzy Petri Nets

2010 ◽  
Vol 26-28 ◽  
pp. 77-82 ◽  
Author(s):  
Lan Yun Li ◽  
Zhuan Zhao Yang ◽  
Zhi He
Keyword(s):  
Fault Diagnosis ◽  
Petri Nets ◽  
Rough Set Theory ◽  
Rough Sets Theory ◽  
Fault Features ◽  
Fuzzy Petri Nets ◽  
Diagnosis Method ◽  
Sets Theory ◽  
Complementary Strategy ◽  
Fault Information

Rough sets theory (RST) and Fuzzy Petri nets (FPN) have been widely used in fault diagnosis. However, RST has the weakness of over-rigidity decision, and FPN has the dimensional disaster problem. In order to solve these shortcomings, according to complementary strategy, a new fault diagnosis method based on integration of RST and FPN was presented. Firstly, RST was applied to remove redundant fault features and simply fault information, so that the minimal diagnostic rules can be obtained and the fault was roughly diagnosed. Secondly, the optimal FPN structure was built and the fault diagnosis was finally realized through matrix operation of FPN. Finally, a diesel engine fault diagnosis example was analyzed, and the results show that the proposed method not only holds the ability of RST for analyzing and reducing data, but also has the advantage of FPN for parallel reasoning, so it has strong engineering practicability and validity.

Fault Diagnosis of Hydraulic Pump Based on Multi-Sensor Data Fusion

2010 ◽  
Vol 33 ◽  
pp. 539-543
Author(s):  
Ying Liu ◽  
Dun Wen Zuo ◽  
Yao Hua Wang ◽  
Jun Han ◽  
Xiao Qiang Yang
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Sensor Data ◽  
Multiple Sensors ◽  
Decision Level ◽  
Multi Sensor Data Fusion ◽  
Level Data ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Novel Method

Due to hydraulic pump’s multiple fault parameters, imprecision of fault diagnosis and bad fuzzy properties, a novel method of data preprocess to remove the noise disturbance and extract the characteristics of parameters, in which the order analysis is applied, is put forward. Then the hydraulic pump’s fault is diagnosed with decision-level data fusion of multiple sensors. The practical results showed that the fault diagnosis method based on D-S proof theory and decision-level data fusion could promote the accuracy and efficiency of hydraulic pump’s fault diagnosis.

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