Return parts analysis - why?
As industries increasingly rely on complex electromechanical systems, the importance of Return Parts Analysis (RPA) cannot be overstated. This crucial process involves forensic examination of failed components to determine root causes and prevent future issues. Let's explore why RPA is essential, particularly for components like valves, sensors, pumps, and electrical boards.
Return Parts Analysis provides invaluable insights into component failures, offering benefits that extend far beyond simple troubleshooting:
1. Root Cause Identification:
RPA helps pinpoint the exact reasons for component failures, whether due to design flaws, manufacturing defects, or operational issues. For example, analysis of failed pressure sensors may reveal that corrosion of electrical contacts is causing premature failures.
The pressure sensors were installed in a coastal industrial facility where high humidity and salt-laden air were prevalent.
Ingress Protection Failure: The sensors' IP67 rated housings were compromised due to improper installation, allowing moisture to penetrate. The electrical contacts were made of copper alloy, which is susceptible to galvanic corrosion when exposed to saltwater. Salt deposits on the contacts formed an electrolyte, accelerating the corrosion process.
This led to the formation of copper chloride, a green corrosion product often called 'verdigris'. The corrosion increased contact resistance, causing signal attenuation and eventual loss of sensor output. Sensors began failing after just 6 months in service, far short of their expected 5-year lifespan. 30% of installed sensors failed within the first year, significantly impacting process reliability and maintenance costs.?
2. Failure Pattern Recognition:
By analyzing multiple failed parts, trends and patterns can emerge, highlighting systemic problems. An RPA team might discover that a particular batch of valves is failing at a higher rate due to a material defect from a specific supplier.
?In this scenario, an RPA team is investigating a series of valve failures. They've collected data on 100 failed valves, including failure types, supplier information, and batch numbers. Here's how they uncovered a systemic problem:
Initial Data Analysis:
?? The team first examined the failure data for Batch 2 valves:
?? - Corrosion: 13 failures
?? - Cracking: 13 failures
?? - Leak: 12 failures
?? - Jam: 12 failures
?This distribution suggested a potential issue with material integrity, as corrosion and cracking were slightly more prevalent. The team noticed that 25% of the valves (25 out of 100) were flagged for material defects. This prompted a closer look at supplier data.
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They compiled data on supplier quality ratings and defect rates:
?? - Supplier A: 80/100 quality rating, 5% defect rate
?? - Supplier B: 75/100 quality rating, 10% defect rate
?? - Supplier C: 90/100 quality rating, 2% defect rate
?? - Supplier D: 85/100 quality rating, 7% defect rate
The RPA team hypothesized that Supplier D might be using a new material or process that's causing unexpected defects, despite their generally good quality rating.
3. Quality Improvement:
Insights from RPA drive enhancements in design, manufacturing processes, and material selection. For instance, analysis of failed electrical boards may lead to changes in PCB coating processes to improve moisture resistance.
In this scenario, an RPA team investigated a series of electrical board failures and uncovered critical insights that led to significant improvements in PCB coating processes. Here's a detailed breakdown of their analysis and findings. The RPA team examined 10 failed electrical boards and found the following:
Key Findings:
Root Cause Analysis:
By conducting this comprehensive analysis and implementing targeted improvements, the RPA team was able to significantly enhance the quality and reliability of their electrical boards.
By investing in a robust Return Parts Analysis process, companies can dramatically improve product reliability, reduce costs, and enhance customer satisfaction. The insights gained from RPA are not just valuable – they're essential for long-term success and innovation in electromechanical systems.
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