arrow_back_ios

Main Menu

See All Software See All Instrumente See All Aufnehmer See All Schwingungsprüfung See All Elektroakustisch See All Akustische End-of-Line-Testsysteme See All Events See All Akademie See All Anwendungen See All Industrien See All Kalibrierung See All Ingenieurdienstleistungen See All Unterstützen
arrow_back_ios

Main Menu

See All Durability See All Reliability See All Analyse Simulation See All DAQ See All API Treiber See All Dienstprogramm See All Vibrationskontrolle See All Kalibrierung See All DAQ See All Handheld See All Industriell See All Power Analyzer See All Signalaufbereiter See All Akustik See All Strom und Spannung See All Weg See All Kraft See All Wägezellen See All Mehrkomponenten See All Druck See All Dehnung See All Dehnungsmessstreifen See All Temperatur See All Neigen See All Drehmoment See All Vibration See All Zubehör See All Steuerungen See All Messerreger See All Modalerreger See All Leistungsverstärker See All Shaker Systeme See All Testlösungen See All Aktoren See All Verbrennungsmotoren See All Betriebsfestigkeit See All eDrive See All Sensoren für Produktionstests See All Getriebe See All Turbolader See All Schulungskurse See All Akustik See All Anlagen- und Prozessüberwachung See All Elektrische Energie See All NVH See All Kundenspezifische OEM-Sensoren See All Strukturelle Integrität See All Schwingbelastung See All Automobil & Bodentransport See All Druckkalibrierung | Sensor | Messumformer See All Kalibrierung oder Reparatur anfordern See All Kalibrierung und Verifizierung See All Kalibrierung Plus Vertrag See All Brüel & Kjær Support
arrow_back_ios

Main Menu

See All Aqira See All nCode Viewer (DE) See All Weibull++ - NEW TEST (DE) See All Weibull++ - NEW TEST (DE) See All BlockSim - New Test (DE) See All BlockSim - New Test (DE) See All XFRACAS - New Test (DE) See All XFMEA - New Test (DE) See All XFMEA - New Test (DE) See All RCM++ - New Test (DE) See All RCM++ - New Test (DE) See All SEP - New Test (DE) See All SEP - New Test (DE) See All Lambda Predict - New Test (DE) See All Lambda Predict - New Test (DE) See All MPC - New Test (DE) See All nCode - Durability and Fatigue Analysis See All ReliaSoft - Reliability Analysis and Management See All API See All Elektroakustik See All Umgebungslärm See All Identifizierung der Lärmquelle See All Produkt-Lärm See All Schallleistung und Schalldruck See All Vorbeifahrgeräusche See All Produktionsprüfung und Qualitätssicherung See All Maschinenanalyse und -diagnose See All Strukturelle Gesundheitsüberwachung See All Strukturüberwachung See All Batterieprüfung See All Einführung in die Messung elektrischer Leistung bei transienten Vorgängen See All Transformator-Ersatzschaltbild | HBM See All OEM-Sensoren für die Landwirtschaft See All OEM-Sensoren für Robotik- und Drehmomentanwendungen See All OEM-Sensoren für die Agrarindustrie See All OEM-Sensoren für Robotik- und Drehmomentanwendungen See All Strukturelle Dynamik See All Prüfung der Materialeigenschaften See All Sicherstellung der strukturellen Integrität von Leichtbaustrukturen See All Elektrifizierung von Fahrzeugen See All Seiten, die nicht migriert wurden See All Software-Lizenzverwaltung

Betriebsfestigkeitstests, Zuverlässigkeit und Fehlermodusvergleich

Let’s assume you are an engineer tasked with validating the durability of a structural part that is susceptible to fatigue cracking. Proving ground tests are often used to assess component durability, but unfortunately they can be costly and time consuming. A laboratory durability test is desirable due to reduced variability, cost, and time but you then face the challenge of moving the durability test into the lab while replicating the same failure behavior. 

 

If fatigue is the failure mode, fatigue methodologies like Stress-Life (SN) can be used to create an equivalent fatigue damage test specification. This way, the complicated loading seen on the proving ground can be converted into simplified test lab loading that produces the same amount of fatigue damage. Once a relative damage relationship has been determined, the lab test can be run and results from the proving ground and lab can be compared statistically with reliability methods to assess the failure behavior and answer these critical questions:  

  • Are these failure modes the same? 
  • Is the lab test representative of real product usage?

Durability testing, reliability, and failure mode comparison

 

Let’s assume you are an engineer tasked with validating the durability of a structural part that is susceptible to fatigue cracking. Proving ground tests are often used to assess component durability, but unfortunately they can be costly and time consuming. A laboratory durability test is desirable due to reduced variability, cost, and time but you then face the challenge of moving the durability test into the lab while replicating the same failure behavior. 

 

If fatigue is the failure mode, fatigue methodologies like Stress-Life (SN) can be used to create an equivalent fatigue damage test specification. This way, the complicated loading seen on the proving ground can be converted into simplified test lab loading that produces the same amount of fatigue damage. Once a relative damage relationship has been determined, the lab test can be run and results from the proving ground and lab can be compared statistically with reliability methods to assess the failure behavior and answer these critical questions:  

  • Are these failure modes the same? 
  • Is the lab test representative of real product usage?

This process can be performed in three general steps:

Step 1: Assessing fatigue cyclic content

 

The structural loading this part experiences in the proving ground has been measured and is shown above. This measured data contains a broad spectrum of cycles – from small to large – all of which contribute to the overall fatigue damage.  

 

The most common method of quantifying cyclic content for fatigue calculations is rainflow cycle counting.  Results are shown below.

Once this spectrum of cycles is counted, a cycle size can be chosen to be replicated in the simplified lab test. The cycle size should not be larger than the largest cycle observed in the measured loading or else the failure mode may be changed. Additionally, a very small cycle size might require a very lengthy test in order to replicate equivalent fatigue damage. Therefore, a balance must be made to select a cycle size that is large enough for the lab test to complete within a target duration, yet not too large.

 

Step 2: Calculating equivalent damage loading

 

The final step is to compare the failure behavior from the track test and lab test(s). It is possible to perform forensics on the broken faces of a failed part to assess the failure mode. Alternatively, the life data results from each population of failed parts can be modelled statistically and compared. While several statistical models could be used, Weibull is often selected due to its flexibility. The parameters β and η can describe the failure behavior and time to failure respectively. When using a level of confidence, these parameters can then be described as ranges.

Step 3: Failure mode comparison

 

Suppose you followed the steps above and create two possible test loading specs:

  • Lab test A: A large cycle repeated 50,000 times
  • Lab test B: A small cycle repeated 200,000 repeats

Test A has clear benefits in that it will take less time to get results! But maybe you want to know which option is a better durability test. 

 

You run the product validation test using both newly-created equivalent damage lab loadings, failing 6 samples in each test. Fatigue damage calculations allow the lab failure times to be correlated back to equivalent proving ground laps. Previous testing on the proving ground also returned 6 failures.

The final step is to compare the failure behavior from the proving ground and lab test(s). It is common to perform forensics on the broken faces of broken parts to assess the failure mode – with the assumption that similar failure surfaces implies similar failure modes.

 

Statistics can also aid this comparison. The life data results from each population of failed parts can be statistically modelled and compared. While several statistical models could be used, the Weibull distribution is often selected due to its flexibility. Weibull parameters β and η can describe the failure behavior and time to failure, respectively. These values can be calculated for each set of failure data and then compared to assess similarities or differences. Further, confidence bounds can be introduced and these parameters can then be described as ranges.

 

Once each population is modeled, β and η at a specified level of confidence can be compared. Visually, the easiest comparison method is on a contour plot. Separation between contours signifies populations are statistically different at the specified level of confidence.

This plot shows several contours of Weibull parameters: black is lab test A, red is lab test B, and orange is the proving ground. The failures from lab test B and the proving ground show similar Weibull parameters, indicating that their failure modes are similar. Lab test A shows drastically different Weibull characteristics, indicative of a different failure mode entirely. This provides us with statistical evidence that while lab test A has the benefit of a shorter test duration, it’s actually resulted in a different failure mode! 

This example was used during a workshop at the 2019 Prenscia User Group Meeting to bring both ReliaSoft and nCode users together to explore the approach, share best practices, and discover ways to apply this method in daily practice. We have found that this approach works well for creating lab test from proving grounds with equivalent damage. We are confident that if you need to create a lab test specification, this method will allow you to reduce test time and cost while assessing failure mode behavior. 

 

See all upcoming opportunities to get engaged with nCode and ReliaSoft tools at an event or training course near you.

Ready to take your reliability program further?