We acknowledge the support of the Natural Sciences and Engineering Council of Canada (NSERC), which invests annually over $1 billion in people, discovery and innovation. EECOMOBILITY (ORF) & HEVPD&D CREATE We acknowledge the support of the Ontario Research Fund: Research Excellence Program. Advanced Fault Detection and Diagnosis Centre for Mechatronics and Hybrid Technology Mechanical Engineering McMaster University Mahmoud Ismail WHY FAULT DETECTION & DIAGNOSIS? SOUND & VIBRATION WHY SOUND & VIBRATION IS COMPLEX IEMSPCA DEEP - LEARNING AI COMMERCIAL IMPLEMENTATION D&V • The sound and vibration signals produced by rotary equipment is a result of their design, materials, and operating conditions. When a rotary equipment is faulty, it produces different sound and vibration signatures compared to a healthy baseline. • Being able to detect the difference in sound and vibration enables fault detection and diagnosis. Vibration Sound Quality control: Proactive Maintenance: Production line Quality control Pass Reject Satisfied customer Shipping Machine health monitoring Normal operation Fault detected Fault diagnosis Critical fault Continue operation No Raise a flag Stop operation and raise and flag Yes Benefits: Satisfied customers – warranty cost reduction – downtime cost reduction Benefits: Maintenance and downtime cost reduction – better operation predictability Many systems reject products that produce noise levels above a certain threshold but that is not enough as many faults and production problems such as excessive lubricant (grease) can result in quieter noise levels Spectrum analysis can be very useful as it shows another dimension of sound and vibration measurements in the frequency domains. However it is very hard to relate the changes in frequency domain to the machine condition which makes its usage limited. Amplitude Spectrum Complexity: to successfully use sound and vibration measurements to detect and diagnose faults, the analysis algorithm should take into consideration the changes in both time and frequency domains. Whether the changes are an increase or a decrease in sound and vibration levels. Collect Measurements Background Noise Filtration Wavelet Analysis PCA Analysis Fault Signature Classifier Healthy Fault #1 Fault #2 Fault #3 Results IEMPSCA Algorithm IEMSPCA algorithm uses the difference in time and frequency domains to detect any deviation from baseline measurements. If there is any deviation baseline, further analysis is done on the fault signature to diagnose the fault type. IEMPSCA was tested and achieved 100% fault detection rates and 96.8% from fault diagnosis. Background noise filtration is an important step to eliminate any confusion in the results. Wavelet analysis is able to analyze the measurements both in time and frequency domains. PCA is able to detect any changes in the time and frequency domains produced by wavelet analysis Deep Learning is the leading Artificial Intelligence algorithm in many industries such as speech and image recognition. The Centre for Mechatronics and Hybrid Technology (CMHT) applied Deep Learning on fault detection and diagnosis applications. 100% fault detection success rates and 97.6% accuracy for fault diagnosis were achieved. Collect Measurements Healthy Fault #1 Fault #2 Fault #3 Both technologies, IEMSPCA and Deep Learning are implemented for commercial use in D&V Electronics SV-PRO software. which is designed to provide different tools for sound and vibration analysis including traditional and advanced analysis types.

Advanced Fault Detection and Diagnosis · Mahmoud Ismail WHY FAULT DETECTION & DIAGNOSIS? SOUND & VIBRATION WHY SOUND & VIBRATION IS COMPLEX IEMSPCA DEEP-LEARNING AI COMMERCIAL IMPLEMENTATION

Download PDF Report

Upload
others
View
15
Download
0

Embed Size (px)

Citation preview

Page 1: Advanced Fault Detection and Diagnosis · Mahmoud Ismail WHY FAULT DETECTION & DIAGNOSIS? SOUND & VIBRATION WHY SOUND & VIBRATION IS COMPLEX IEMSPCA DEEP-LEARNING AI COMMERCIAL IMPLEMENTATION

We acknowledge the support of the NaturalSciences and Engineering Council of Canada(NSERC), which invests annually over $1billion in people, discovery and innovation.

EECOMOBILITY (ORF) &HEVPD&D CREATE

We acknowledge the supportof the Ontario Research Fund:Research Excellence Program.

Advanced Fault Detection and DiagnosisCentre for Mechatronics and Hybrid TechnologyMechanical Engineering McMaster UniversityMahmoud Ismail

WHY FAULT DETECTION & DIAGNOSIS?

SOUND & VIBRATION

WHY SOUND & VIBRATION IS COMPLEX

IEMSPCA

DEEP-LEARNING AI

COMMERCIAL IMPLEMENTATION D&V

• The sound and vibration signals produced by rotary equipment is a result of their design,materials, and operating conditions. When a rotary equipment is faulty, it produces differentsound and vibration signatures compared to a healthy baseline.

• Being able to detect the difference in sound and vibration enables fault detection anddiagnosis.

Vibration

Sound

Quality control:

Proactive Maintenance:

Production line Quality control

Pass

Reject

Satisfied customer

Shipping

Machine health monitoring

Normal operation

Fault detected

Fault diagnosis

Critical fault

Continue operation

Raise a flag

Stop operation and raise and flag

Yes

Benefits: Satisfied customers –warranty cost reduction –downtime cost reduction

Benefits: Maintenance and downtime cost reduction – better operation predictability

Many systems reject products thatproduce noise levels above a certainthreshold but that is not enough as manyfaults and production problems such asexcessive lubricant (grease) can result inquieter noise levels

Spectrum analysis can be very useful as itshows another dimension of sound andvibration measurements in the frequencydomains. However it is very hard to relate thechanges in frequency domain to the machinecondition which makes its usage limited.

Amplitude Spectrum

Complexity: to successfully use sound and vibration measurements to detect and diagnosefaults, the analysis algorithm should take into consideration the changes in both time andfrequency domains. Whether the changes are an increase or a decrease in sound andvibration levels.

Collect Measurements

Background Noise Filtration

Wavelet Analysis

PCA Analysis

Fault Signature Classifier

Healthy

Fault #1

Fault #2

Fault #3

Results

IEMPSCA Algorithm

IEMSPCA algorithm uses the difference in time and frequency domains to detect anydeviation from baseline measurements. If there is any deviation baseline, further analysis isdone on the fault signature to diagnose the fault type. IEMPSCA was tested and achieved100% fault detection rates and 96.8% from fault diagnosis.

Background noise filtration is an important step to eliminate any confusion in the results.

Wavelet analysis is able toanalyze the measurementsboth in time and frequencydomains.

PCA is able to detect anychanges in the time andfrequency domains producedby wavelet analysis

Deep Learning is the leading Artificial Intelligence algorithm in many industries such asspeech and image recognition. The Centre for Mechatronics and Hybrid Technology (CMHT)applied Deep Learning on fault detection and diagnosis applications. 100% fault detectionsuccess rates and 97.6% accuracy for fault diagnosis were achieved.

Collect Measurements

Healthy

Fault #1

Fault #2

Fault #3

Both technologies, IEMSPCA and Deep Learningare implemented for commercial use in D&VElectronics SV-PRO software. which is designed toprovide different tools for sound and vibrationanalysis including traditional and advancedanalysis types.