scholarly journals MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection

2019 ◽  
Author(s):  
Harsh Purohit ◽  
Ryo Tanabe ◽  
Takeshi Ichige ◽  
Takashi Endo ◽  
Yuki Nikaido ◽  
...  
Keyword(s):  
Industrial Machine

scholarly journals Text Mining for Industrial Machine Predictive Maintenance with Multiple Data Sources

2021 ◽  
Author(s):  
Giancarlo Nota ◽  
Alberto Postiglione
Keyword(s):  
Data Structure ◽  
Text Mining ◽  
Machine Tool ◽  
Computational Effort ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Log Files ◽  
Multiple Data ◽  
Innovative Methodology ◽  
Industrial Machine

This paper presents an innovative methodology, from which an efficient system prototype is derived, for the algorithmic prediction of malfunctions of a generic industrial machine tool. It integrates physical devices and machinery with Text Mining technologies and allows the identification of anomalous behaviors, even of minimal entity, rarely perceived by other strategies in a machine tool. The system works without waiting for the end of the shift or the planned stop of the machine. Operationally, the system analyzes the log messages emitted by multiple data sources associated with a machine tool (such as different types of sensors and log files produced by part programs running on CNC or PLC) and deduces whether they can be inferred from them future machine malfunctions. In a preliminary offline phase, the system associates an alert level with each message and stores it in a data structure. At runtime, three algorithms guide the system: pre-processing, matching and analysis: Preprocessing, performed only once, builds the data structure; Matching, in which the system issues the alert level associated with the message; Analysis, which identifies possible future criticalities. It can also analyze an entire historical series of stored messages The algorithms have a linear execution time and are independent of the size of the data structure, which does not need to be sorted and therefore can be updated without any computational effort.

scholarly journals Industrial Machine Identification Using Augmented Reality

2021 ◽  
Vol 3 (Special Issue ICARD 3S) ◽  
pp. 68-71
Author(s):  
Kavya Shakthi R.P ◽  
Kavin Raja A.S ◽  
Janani S.R ◽  
Sangeetha K
Keyword(s):  
Augmented Reality ◽  
Industrial Machine

Feature Selection for Audio-Based Fault Detection in Pumps

2020 ◽  
Author(s):  
Victor O. Adegboye ◽  
Jason H. Rife
Keyword(s):  
Fault Detection ◽  
Ambient Noise ◽  
Area Under The Curve ◽  
Mel Frequency Cepstral Coefficients ◽  
Common Features ◽  
Performance Metric ◽  
Audio Recordings ◽  
Selection For ◽  
Industrial Machine

Abstract Whilst extensive work has been done on fault detection in bearings using sound, very little has been accomplished with other machine components and machinery partly due to the scarcity of datasets. The recent release of the Malfunctioning Industrial Machine Investigation and Inspection (MIMII) dataset opens the opportunity for research into malfunctioning machines like pumps, fans, slide rails, and valves. In this paper, we compare common features from audio recordings to investigate which best support the classification of malfunctioning pumps. We evaluate our results using the Area Under the Curve (AUC) as a performance metric and determine that the log mel spectrum is a very useful feature, at least for this dataset, but that other features can enhance detection performance when ambient noise is present (improving AUC from 0.88 to 0.94 in one case). Also, we find that mel Frequency Cepstral Coefficients (MFCC) perform substantially poorer as features than a sampled mel spectrogram.

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