Template:Warranty analysis - Revision history

Kate Racaza: Redirected page to Warranty Data Analysis

2012-06-29T07:44:43Z

← Older revision		Revision as of 07:44, 29 June 2012
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	~~The~~ Warranty Analysis Folio in Weibull++ provides three different data entry formats for warranty data. It also allows the user to automatically perform a life data analysis on this type of data, allows the prediction of future failures through the use of conditional probability analysis and provides methods for detecting outliers.		#REDIRECT [[Warranty Data Analysis]]

	~~{{Warranty Data Entry Types}}~~

	~~{{warranty analysis-nevada format}}~~

	~~{{warranty analysis-usage format}}~~

	~~{{warranty prediction}}~~

	~~{{analysis of non-homogeneous warranty data}}~~

	~~{{monitoring warranty returns using statistical process control}}~~

2012-02-21T18:33:18Z

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 The Warranty Analysis Folio in Weibull++ provides three different data entry formats for warranty data. It also allows the user to automatically perform a life data analysis on this type of data, allows the prediction of future failures through the use of conditional probability analysis and provides methods for detecting outliers.
-===Warranty Data Entry Types===
+{{Warranty Data Entry Types}}
 {{warranty analysis-nevada format}}

2012-02-21T18:29:51Z

← Older revision		Revision as of 18:29, 21 February 2012
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	==Warranty Analysis==		The Warranty Analysis Folio in Weibull++ provides three different data entry formats for warranty data. It also allows the user to automatically perform a life data analysis on this type of data, allows the prediction of future failures through the use of conditional probability analysis and provides methods for detecting outliers.

	The Warranty Analysis Folio in Weibull++ provides three different data entry formats for warranty data. It also allows the user to automatically perform a life data analysis on this type of data, allows the prediction of future failures through the use of conditional probability analysis and provides methods for detecting outliers.
	===Warranty Data Entry Types===		===Warranty Data Entry Types===
	Weibull++ allows for data entry utilizing three common formats for storing sales and returns information.		Weibull++ allows for data entry utilizing three common formats for storing sales and returns information.

2012-01-04T23:45:25Z

Monitoring Warranty Returns Using Statistical Process Control (SPC)

2012-01-04T23:44:19Z

Analysis of Non-Homogeneous Warranty Data

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 {{warranty prediction}}
-===Analysis of Non-Homogeneous Warranty Data===
+{{analysis of non-homogeneous warranty data}}
 ===Monitoring Warranty Returns Using Statistical Process Control (SPC)===

2012-01-04T23:43:35Z

Warranty Prediction

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 {{warranty analysis-usage format}}
-===Warranty Prediction===
+{{warranty prediction}}
 ===Analysis of Non-Homogeneous Warranty Data===

2012-01-04T23:43:06Z

Warranty Analysis (Usage Format)

@@ Line 26: / Line 26: @@
 {{warranty analysis-nevada format}}
-===Warranty Analysis (Usage Format)===
+{{warranty analysis-usage format}}
 ===Warranty Prediction===

2012-01-04T23:42:31Z

Warranty Analysis (Nevada Format)

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 {{usage format}}
-===Warranty Analysis (Nevada Format)===
+{{warranty analysis-nevada format}}
 ===Warranty Analysis (Usage Format)===

2012-01-04T23:42:00Z

Usage Format

← Older revision		Revision as of 23:42, 4 January 2012
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	{{dates of failure format}}		{{dates of failure format}}

	~~====Usage Format====~~		{{usage format}}
	~~[[Image:usageformat1.png\|thumb\|center\|400px\| ]]~~

	~~Often the driving factor for reliability is~~ usage ~~rather than time. For example, in the automotive industry, the failure behavior in the majority of the products is mileage-dependent rather than time-dependent. This~~ format ~~allows specifying Quantity In-Service Data, and the usage at return date and Quantity Return Data, as illustrated in the following tables.~~

	~~<center>'''Quality In-Service Data'''</center>~~
	~~[[Image:crazedquantity.png\|thumb\|center\|400px\| ]]~~


	~~[[Image:monthsquantity.png\|thumb\|center\|400px\| ]]~~

	Note that, like in the dates of failure format, in order to identify which lot the unit comes from, a failure is identified by the return number and the date of when it was put in service. The date that the returned unit went into service associates the returned unit with the lot it belonged to when it started operation. However, the return data is in terms of usage and not date of return. Therefore the usage of the units needs to be specified as a constant usage per unit time or as a distribution. This allows determining the expected usage of the surviving units (See Example 3).

	===Warranty Analysis (Nevada Format)===		===Warranty Analysis (Nevada Format)===

2012-01-04T23:40:11Z

Dates of Failure Format

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 {{time-to-failure format}}
-====Dates of Failure Format====
+{{dates of failure format}}
 ====Usage Format====