Development of an Explainable Fault Diagnosis Framework Based on Sensor Data Imagification: A Case Study of the Robotic Spot-Welding Process

2021 ◽  
pp. 1-1
Author(s):  
Jiwoong Lee ◽  
Inwoong Noh ◽  
Jihyun Lee ◽  
Sang Won Lee
Keyword(s):  
Fault Diagnosis ◽  
Spot Welding ◽  
Welding Process ◽  
Sensor Data

scholarly journals Application of PCA and Classification for Fault Diagnosis of MAB Installed in Petrochemical Plant Process Facilities

2021 ◽  
Vol 11 (9) ◽  
pp. 3780
Author(s):  
Se-Yun Hwang ◽  
Kwang-Sik Kim ◽  
Hyung-Jin Kim ◽  
Hong-Bae Jun ◽  
Jang-Hyun Lee
Keyword(s):  
Fault Diagnosis ◽  
Operating Conditions ◽  
Suggested Method ◽  
Sensor Data ◽  
Actual Process ◽  
Petrochemical Plant ◽  
Pca Method ◽  
Diagnosis Model ◽  
Plant Process

In large systems, such as power plants or petrochemical plants, various equipment (e.g., compressors, pumps, turbines, etc.) are typically deployed. Each piece of equipment operates under generally harsh operating conditions, depending on its purpose, and operates with a probability of failure. Therefore, several sensors are attached to monitor the status of each piece of equipment to observe its conditions; however, there are many limitations in monitoring equipment using thresholds such as maximum and minimum values of data. Therefore, this study introduces a technology that can diagnose fault conditions by analyzing several sensor data obtained from plant operation information systems. The equipment for the case study was a main air blower (MAB), an important cooling equipment in the plant process. The driving sensor data were analyzed for approximately three years, measured at the plant. The fault history of the actual process was also analyzed. Due to the large number of sensors installed in the MAB system, a dimension reduction method was applied with the principal component analysis (PCA) method when analyzing collected sensor data. For application to PCA, the collected sensor data were analyzed according to the statistical analysis method and data features were extracted. Then, the features were labeled and classified according to normal and fault operating conditions. The analyzed features were converted with a diagnosis model, by dimensional reduction, applying the PCA method and a classification algorithm. Finally, to validate the diagnosis model, the actual failure signal that occurred in the plant was applied to the suggested method. As a result, the results from diagnosing signs of failure were confirmed even before the failure occurred. This paper explains the case study of fault diagnosis for MAB equipment with the suggested method and its results.

scholarly journals SUITABLE PRODUCTION TOOLS SELECTION WITH THE USE OF EVOLUTIONARY ALGORITHMS

2020 ◽  
Vol 60 (1) ◽  
pp. 56-64
Author(s):  
Petr Hynek ◽  
Viktor Kreibich ◽  
Roman Firt
Keyword(s):  
Evolutionary Algorithms ◽  
Automotive Industry ◽  
Spot Welding ◽  
Production Systems ◽  
Welding Process ◽  
Production Equipment ◽  
Welding Equipment ◽  
Simulation Results ◽  
Selection Of

This paper deals with the use of a production equipment simulation in the design of production systems, more specifically the welding equipment in the automotive industry. Based on the simulation results, a matrix, which defines the possibility of using given manufacturing tools (in this case welding guns are considered) to connect the plates using the electrical resistance spot welding process, is created. This matrix generates a set of several numbers of solutions depending on other parameters, such as the lowest price, the lowest number of used welding guns, etc. The goal is to solve this task. The solution is presented using mathematical programming. Specifically, the method of genetic evolutionary algorithms is being used. The Solver software is used to optimize the selection of the welding guns’ combination. The Solver is an add-on in MS Excel. The case study shows 15 welding points weldment on which the availability of 20 types of welding guns was simulated. The result is an ideal combination of 2 types of guns for the lowest price.

scholarly journals Deep Learning-based Approach for Fault Diagnosis of Rolling Element Bearings

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Mohammadkazem Sadoughi ◽  
Austin Downey ◽  
Garrett Bunge ◽  
Aditya Ranawat ◽  
Chao Hu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Signal To Noise Ratio ◽  
Health Condition ◽  
Diagnostic Information ◽  
Sensor Data ◽  
Spectral Kurtosis ◽  
Shaft Misalignment ◽  
Processing Techniques

Condition monitoring and fault detection of roller element bearings is of vital importance to ensuring safe and reliable operation of rotating machinery systems. Over the past few years, convolutional neural network (CNN) has been recognized as a useful tool for fault detection of roller element bearings. Unlike the traditional fault diagnosis approaches, CNN does not require manually extracting the fault-related features from the raw sensor data and most CNN-based fault diagnosis approaches feed the raw or shallowly pre-processed data as the training/testing inputs to a CNN model, thereby avoiding the need for manual feature extraction. As such, these approaches can be considered as purely data-driven. However, it has been proven that some well-established signal pre-processing techniques such as spectral kurtosis and envelope analysis can effectively clean and pre-process a raw signal to be a better representative of the health condition of a bearing without losing critical diagnostic information. This study proposes a new approach to bearing fault diagnosis, termed the SK-based multi-channel CNN (SCNN), that combines signal pre-processing techniques with a modified 1D CNN. The proposed SCNN approach involves two main steps: in the first step, each raw sensor signal acquired from a bearing is pre-processed to maximize the signal-to-noise ratio without losing critical diagnostic information carried by the signal; and in the second step, all pre-processed signals are fed into a 1D multi-channel CNN that classifies the health condition of the bearing. An experimental case study was carried out to evaluate the performance of the proposed approach. In this case study, a machinery fault simulator was used to validate the performance of SCNN in the presence of faults unrelated to bearings such as shaft misalignment and rotor unbalance.

Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Planetary Gearbox ◽  
Aerial Vehicle

Case Study on Fault Diagnosis of the Actual Operating Transformer by FRA

2011 ◽  
Vol 131 (1) ◽  
pp. 78-85 ◽  
Author(s):  
Takahiro Sano ◽  
Yoshiharu Ogawa ◽  
Takaaki Shimonosono ◽  
Tadayuki Wada
Keyword(s):  
Fault Diagnosis ◽  
Actual Operating

THERMOMECHANICAL ANALYSIS OF THE RESISTANCE SPOT-WELDING PROCESS

2018 ◽  
Vol 10 (5) ◽  
pp. 449-455 ◽  
Author(s):  
Habib Lebbal ◽  
Lahouari Boukhris ◽  
Habib Berrekia ◽  
Abdelkader Ziadi
Keyword(s):  
Resistance Spot Welding ◽  
Spot Welding ◽  
Welding Process ◽  
Thermomechanical Analysis ◽  
Resistance Spot

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.

scholarly journals Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhe Yang ◽  
Dejan Gjorgjevikj ◽  
Jianyu Long ◽  
Yanyang Zi ◽  
Shaohui Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Supervised Classification ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Reconstruction Error ◽  
Fine Tuning ◽  
Fault Diagnostics ◽  
Sparse Autoencoder

AbstractSupervised fault diagnosis typically assumes that all the types of machinery failures are known. However, in practice unknown types of defect, i.e., novelties, may occur, whose detection is a challenging task. In this paper, a novel fault diagnostic method is developed for both diagnostics and detection of novelties. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that its performance is satisfactory both in detection of novelties and fault diagnosis, outperforming other state-of-the-art methods. This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect, but also detect unknown types of defects.

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