Faults diagnosis using self-organizing maps: A case study on the DAMADICS benchmark problem

Katunin, Andrzej; Amarowicz, Marcin; Chrzanowski, Paweł

doi:10.15439/2015f26

Cited by 8 publications

(4 citation statements)

References 37 publications

(35 reference statements)

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“…Since the LAMSTAR network alone is close to a vanilla SOM model, the better classification performance obtained by LAMSTAR-DL network shows the advantage of LAMSTAR-DL over the simple SOM model. With limited amount of studies on SOM-based fault diagnosis, the simple SOM-based methods suffer from the weaknesses, including (a) time-consuming, 70 (b) weak identification ability, 71 and (c) dependence on faulty feature extraction and selection. 72 In comparison with diagnosis results obtained by using tradition signal processing technique reported, 27 the presented method achieved higher accuracy without feature extraction process.…”

Section: Validation Resultsmentioning

confidence: 99%

Wind turbine planetary gearbox feature extraction and fault diagnosis using a deep-learning-based approach

Yoon

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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Planetary gearboxes are widely used in the drivetrain of wind turbines. Planetary gearbox fault diagnosis is very important for reducing the downtime and maintenance cost and improving the safety, reliability, and life span of the wind turbines. The wind energy industry is currently using condition monitoring systems to collect massive real-time data and conventional vibratory analysis as a standard method for planetary gearbox condition monitoring. As an attractive option to process big data for fault diagnosis, deep learning can automatically learn features that otherwise require much skill, time, and experience. This article presents a new deep-learning-based method for wind turbine planetary gearbox fault diagnosis developed by a large memory storage and retrieval neural network with dictionary learning. The developed approach can automatically extract self-learned fault features from raw vibration monitoring data and perform planetary gearbox fault diagnosis without supervised fine-tuning process. From the raw vibration monitoring data, a dictionary is first learned by a large memory storage and retrieval with dictionary learning network. Based on the learned dictionary, a sparse representation of the raw vibration signals is generated by shift-invariant sparse coding and input to a large memory storage and retrieval network classifier to obtain fault diagnosis results. The structure of the large memory storage and retrieval with dictionary learning is determined by optimal selection of the sliding box size to generate sub-patterns from the vibration data. The effectiveness of the presented method is tested and validated with a set of seeded fault vibration data collected at a planetary gearbox test rig in laboratory. The validation results have shown a promising planetary gearbox fault diagnosis performance with the presented method.

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Section: Validation Resultsmentioning

confidence: 99%

Wind turbine planetary gearbox feature extraction and fault diagnosis using a deep-learning-based approach

Yoon

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…In the detection and identification of faults, the composition of the benchmark model makes it possible to simulate a set of nineteen faults (F1, F2, … F19) of the actuator, classified into four distinct groups: control valve failures (F1 … F7); pneumatic servomotor faults (F8 … F11), positioner faults (F12 … F14) and general faults/external faults (F15 … F19). Thus, the faults previously chosen by the DAMADICS actuator to act in the didactic plant were: obstruction fault (F1), sedimentation fault (F2), and erosion fault (F3) [ 37 , 38 ].…”

Section: Case Studymentioning

confidence: 99%

Development of a Methodology Using Artificial Neural Network in the Detection and Diagnosis of Faults for Pneumatic Control Valves

Andrade

Lopes

Lima

et al. 2021

Sensors

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To satisfy the market, competition in the industrial sector aims for productivity and safety in industrial plant control systems. The appearance of a fault can compromise the system’s proper functioning process. Therefore, Fault Detection and Diagnosis (FDD) methods contribute to avoiding any undesired events, as there are techniques and methods that study the detection, isolation, identification and, consequently, fault diagnosis. In this work, a new methodology that uses faults emulation to obtain parameters similar to the Development and Application of Methods for Diagnosis of Actuators in Industrial Control Systems (DAMADICS) benchmark model will be developed. This methodology uses previous information from tests on sensors with and without faults to detect and classify the situation of the plant and, in the presence of faults, perform the diagnosis through a process of elimination in a hierarchical manner. In this way, the definition of residue signature is used as well as the creation of a decision tree. The whole process is carried out incorporating FDD techniques, through the Non-Linear Auto-Regressive Neural Network Model With Exogenous Inputs (NARX), in the diagnosis of the behavioral prediction of the signals to generate the residual values. Then, it is applied to the construction of the decision tree based on the most significant residue of a certain signal, enabling the process of acquisition and formation of the signature matrix. With the procedures in this article, it is possible to demonstrate a practical and systematic method of how to emulate faults for control valves and the possibility of carrying out an analysis of the data to acquire signatures of the fault behavior. Finally, simulations resulting from the most sensitized variables for the production of residuals that is generated by neural networks are presented, which are used to obtain signatures and isolate the flaws. The process proves to be efficient in computational time and makes it easy to present a fault diagnosis strategy that can be reproduced in other processes.

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“…In [7] deviations from operating conditions are detected and classified to define new clusters for classification purposes, allowing the isolation of new faults. Commonly, data clustering around normal and abnormal operating points is carried out using classification or feature extraction algorithms [8,9].…”

Section: Introductionmentioning

confidence: 99%

A new scheme for multiple fault detection and isolation for rotational mechatronic systems through analytical redundancy and adaptive filtering

López

2019

DYNA

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Although Fault Detection and Isolation systems have been widely studied in recent years, it is still a very active research field due to its relevance in industrial production systems. In this paper, a new approach for multiple fault detection by using residual evaluation is proposed. First, an analytical redundancy scheme for residual generation is applied using nonlinear autoregressive networks with exogenousinputs for normal and faulty conditions. Simultaneous fault data is included in the training set in order to ensure multiple fault detection.Then, an adaptive filter considering statistic measures from input is used to increase sensibility and robustness. Filter coefficients are obtained off-line through genetic algorithm optimization. Finally, a neural network classifier is used for fault isolation. The proposed algorithm is tested on a rotary mechatronic test bench for backlash, bearing static friction and transmission faults to show the effectiveness of the proposed detection.

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Faults diagnosis using self-organizing maps: A case study on the DAMADICS benchmark problem

Cited by 8 publications

References 37 publications

Wind turbine planetary gearbox feature extraction and fault diagnosis using a deep-learning-based approach

Wind turbine planetary gearbox feature extraction and fault diagnosis using a deep-learning-based approach

Development of a Methodology Using Artificial Neural Network in the Detection and Diagnosis of Faults for Pneumatic Control Valves

A new scheme for multiple fault detection and isolation for rotational mechatronic systems through analytical redundancy and adaptive filtering

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