scholarly journals Fault Detection of Electric Impact Drills and Coffee Grinders Using Acoustic Signals

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 269 ◽  
Author(s):  
Adam Glowacz
Keyword(s):  
Fault Detection ◽  
Ball Bearing ◽  
Acoustic Signals ◽  
Rotor Blades ◽  
Total Efficiency ◽  
Nearest Neighbour ◽  
Sliding Bearing ◽  
Detection Techniques ◽  
Increasing Demand ◽  
Paper Classification

Increasing demand for higher safety of motors can be noticed in recent years. Developing of new fault detection techniques is related with higher safety of motors. This paper presents fault detection technique of an electric impact drill (EID), coffee grinder A (CG-A), and coffee grinder B (CG-B) using acoustic signals. The EID, CG-A, and CG-B use commutator motors. Measurement of acoustic signals of the EID, CG-A, and CG-B was carried out using a microphone. Five signals of the EID are analysed: healthy, with 15 broken rotor blades (faulty fan), with a bent spring, with a shifted brush (motor off), with a rear ball bearing fault. Four signals of the CG-A are analysed: healthy, with a heavily damaged rear sliding bearing, with a damaged shaft and heavily damaged rear sliding bearing, motor off. Three acoustic signals of the CG-B are analysed: healthy, with a light damaged rear sliding bearing, motor off. Methods such as: Root Mean Square (RMS), MSAF-17-MULTIEXPANDED-FILTER-14 are used for feature extraction. The MSAF-17-MULTIEXPANDED-FILTER-14 method is also developed and described in the paper. Classification is carried out using the Nearest Neighbour (NN) classifier. An acoustic based analysis is carried out. The results of the developed method MSAF-17-MULTIEXPANDED-FILTER-14 are very good (total efficiency of recognition of all classes—TED = 96%, TECG-A = 97%, TECG-B = 100%).

scholarly journals Recognition of Acoustic Signals of Commutator Motors

2018 ◽  
Vol 8 (12) ◽  
pp. 2630 ◽  
Author(s):  
Adam Glowacz
Keyword(s):  
Fault Detection ◽  
Extraction Method ◽  
Acoustic Signals ◽  
Rotor Blades ◽  
Gear Train ◽  
Detection Methods ◽  
Support Vector ◽  
Total Efficiency ◽  
Feature Extraction Method ◽  
Selection Of

Most faults can stop a motor, and time is lost in fixing the damaged motor. This is a reason why it is essential to develop fault-detection methods. This paper describes the acoustic-based fault detection of two commutator motors: the commutator motor of an electric impact drill and the commutator motor of a blender. Acoustic signals were recorded by a smartphone. Five states of the electric impact drill and three states of the blender were analysed: for the electric impact drill, these states were healthy, damaged gear train, faulty fan with five broken rotor blades, faulty fan with 10 broken rotor blades, and shifted brush (motor off); for the blender, these states were healthy, faulty fan with two broken rotor blades, and faulty fan with five broken rotor blades. A feature extraction method, MSAF-RATIO-27-MULTIEXPANDED-4-GROUPS (Method of Selection of Amplitudes of Frequency Ratio of 27% Multiexpanded 4 Groups), was developed and used for the computation of feature vectors. The nearest mean (NM) and support vector machine (SVM) classifiers were used for data classification. Analysis of the recognition of acoustic signals was carried out. The analysed value of TEEID (the total efficiency of recognition of the electric impact drill) was equal to 96% for the NM classifier and 88.8% for SVM. The analysed value of TEB (the total efficiency of recognition of the blender) was equal to 100% for the NM classifier and 94.11% for SVM.

NEAT: A Resilient Deep Representational Learning for Fault Detection using Acoustic Signals in IIoT Environment

2021 ◽  
pp. 1-1
Author(s):  
Muhammad Aslam Jarwar ◽  
Sunder Ali Khowaja ◽  
Kapal Dev ◽  
Mainak Adhikari ◽  
Saqib Hakak
Keyword(s):  
Fault Detection ◽  
Acoustic Signals

New Preemptive Arc-Fault Detection Techniques in Medium-Voltage Switchgear and Motor Controls

2016 ◽  
Vol 52 (1) ◽  
pp. 740-750 ◽  
Author(s):  
John A. Kay ◽  
G. Amjad Hussain ◽  
Matti Lehtonen ◽  
Lauri Kumpulainen
Keyword(s):  
Fault Detection ◽  
Medium Voltage ◽  
Detection Techniques ◽  
Motor Controls ◽  
Medium Voltage Switchgear

Material Based Fault Detection Methods for PV Systems

2020 ◽  
Vol 865 ◽  
pp. 111-115
Author(s):  
Khaled Osmani ◽  
Ahmad Haddad ◽  
Thierry Lemenand ◽  
Bruno Castanier ◽  
Mohamad Ramadan
Keyword(s):  
Fault Detection ◽  
Raw Materials ◽  
Raw Material ◽  
Detection Methods ◽  
Critical Aspect ◽  
Pv System ◽  
Detection Techniques ◽  
Pv Systems ◽  
Fault Type ◽  
Pv Cell

The overall efficiency of a PV system is strongly affected by the PV cell raw materials. Since a reliable renewable energy source is expected to produce maximum power with longest lifetime and minimum errors, a critical aspect to bear in mind is the occurrence of PV faults according to raw material types. The different failure scenarios occurring in PV system, decrease its output power, reduce its life expectancy and ban the system from meeting load demands, yielding to severe consecutive blackouts. This paper aims first to present different core materials types, material based fault occurring on the PV cell level and consequently the fault detection techniques corresponding to each fault type.

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