Data-driven and association rule mining-based fault diagnosis and action mechanism analysis for building chillers

2020 ◽  
Vol 216 ◽  
pp. 109957 ◽  
Author(s):  
Jiangyan Liu ◽  
Daliang Shi ◽  
Guannan Li ◽  
Yi Xie ◽  
Kuining Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Driven ◽  
Mechanism Analysis ◽  
Action Mechanism ◽  
Rule Mining

scholarly journals Correlating Espresso Quality with Coffee-Machine Parameters by Means of Association Rule Mining

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 100
Author(s):  
Daniele Apiletti ◽  
Eliana Pastor
Keyword(s):  
Real World ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Driven ◽  
Rule Mining ◽  
Electronic Noses ◽  
Data Driven Approach ◽  
The Many ◽  
Coffee Machine

Coffee is among the most popular beverages in many cities all over the world, being both at the core of the busiest shops and a long-standing tradition of recreational and social value for many people. Among the many coffee variants, espresso attracts the interest of different stakeholders: from citizens consuming espresso around the city, to local business activities, coffee-machine vendors and international coffee industries. The quality of espresso is one of the most discussed and investigated issues. So far, it has been addressed by means of human experts, electronic noses, and chemical approaches. The current work, instead, proposes a data-driven approach exploiting association rule mining. We analyze a real-world dataset of espresso brewing by professional coffee-making machines, and extract all correlations among external quality-influencing variables and actual metrics determining the quality of the espresso. Thanks to the application of association rule mining, a powerful data-driven exhaustive and explainable approach, results are expressed in the form of human-readable rules combining the variables of interest, such as the grinder settings, the extraction time, and the dose amount. Novel insights from real-world coffee extractions collected on the field are presented, together with a data-driven approach, able to uncover insights into the espresso quality and its impact on both the life of consumers and the choices of coffee-making industries.

A Fault Diagnosis Method Based on Constrained Frequent Pattern Trees

2010 ◽  
Vol 39 ◽  
pp. 449-454
Author(s):  
Jiang Hui Cai ◽  
Wen Jun Meng ◽  
Zhi Mei Chen
Keyword(s):  
Fault Diagnosis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Large Scale ◽  
Predicate Logic ◽  
Frequent Pattern ◽  
Rule Mining ◽  
First Order ◽  
Broad Term ◽  
Diagnosis Method

Data mining is a broad term used to describe various methods for discovering patterns in data. A kind of pattern often considered is association rules, probabilistic rules stating that objects satisfying description A also satisfy description B with certain support and confidence. In this study, we first make use of the first-order predicate logic to represent knowledge derived from celestial spectra data. Next, we propose a concept of constrained frequent pattern trees (CFP) along with an algorithm used to construct CFPs, aiming to improve the efficiency and pertinence of association rule mining. The running results show that it is feasible and valuable to apply this method to mining the association rule and the improved algorithm can decrease related computation quantity in large scale and improve the efficiency of the algorithm. Finally, the simulation results of knowledge acquisition for fault diagnosis also show the validity of CFP algorithm.

A Fault Diagnosis Model for Power Transformer Using Association Rule Mining-Based on Rough Set

2014 ◽  
Vol 519-520 ◽  
pp. 1169-1172
Author(s):  
De Wen Wang ◽  
Lin Xiao He
Keyword(s):  
Fault Diagnosis ◽  
Rough Set ◽  
Association Rule ◽  
Association Rule Mining ◽  
Power Transformer ◽  
Monitoring Data ◽  
Rule Mining ◽  
Monitoring Technology ◽  
On Line ◽  
Diagnosis Model

With the development of on-line monitoring technology of electric power equipment, and the accumulation of both on-line monitoring data and off-line testing data, the data available to fault diagnosis of power transformer is bound to be massive. How to utilize those massive data reasonably is the issue that eagerly needs us to study. Since the on-line monitoring technology is not totally mature, which resulting in incomplete, noisy, wrong characters for monitoring data, so processing the initial data by using rough set is necessary. Furthermore, when the issue scale becomes larger, the computing amount of association rule mining grows dramatically, and its easy to cause data expansion. So it needs to use attribute reduction algorithm of rough set theory. Taking the above two points into account, this paper proposes a fault diagnosis model for power transformer using association rule mining-based on rough set.

scholarly journals Association Rule-Based Feature Mining for Automated Fault Diagnosis of Rolling Bearing

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yuan Li ◽  
Jinjiang Wang ◽  
Lixiang Duan ◽  
Tangbo Bai ◽  
Xuduo Wang ◽  
...  
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Matrix ◽  
Fault Classification ◽  
Rule Mining ◽  
Bearing Fault ◽  
New Association

Effective and efficient diagnosis methods are highly demanded to improve system reliability. Comparing with conventional fault diagnosis methods taking a forward approach (e.g., feature extraction, feature selection, and fusion, and then fault diagnosis), this paper presents a new association rule mining method which provides an inverse approach unearthing the underlying relation between labeled defects and extracted features for bearing fault analysis. Instead of evenly dividing methods used in traditional association rule mining, a new association rule mining approach based on the equal probability discretization method is presented in this study. First, a series of extracted features of signal data are discretized following the guideline of equalized probability distribution of the data in order to avoid excessive concentration or decentralized data. Next, the data matrix composed of arrays of discretized features and defect labels is exploited to generate the association rules representing the relation between the features and fault types. Experimental study on a bearing test reveals that the proposed method can generate a series of underlying association rules for bearing fault diagnosis, and the related features selected by the proposed method can be used directly to analyze bearing signals for fault classification and defect severity identification. As a new feature selection method, it possesses prominent superiority compared to traditional PCA, KPCA, and LLE dimension reduction methods.

Sign in / Sign up

Export Citation Format

Share Document