{"id":4164,"date":"2025-11-08T07:06:31","date_gmt":"2025-11-08T12:06:31","guid":{"rendered":"http:\/\/www.livingreliability.com\/en\/?p=4164"},"modified":"2025-11-08T07:06:34","modified_gmt":"2025-11-08T12:06:34","slug":"is-random-failure-really-random","status":"publish","type":"post","link":"https:\/\/www.livingreliability.com\/en\/posts\/is-random-failure-really-random\/","title":{"rendered":"Is “random failure” really random?"},"content":{"rendered":"

The word “Random” might confuse us\u00a0when we refer to Nowlan and Heap’s\u00a0failure pattern B that describes “age independent failure”. \u00a0When using\u00a0the word random applied to an event, we tend to infer that the event is unpredictable. Randomness suggests a non-coherence, such that there is no intelligible pattern or combination that may be discerned. But maintenance and reliability engineers today\u00a0\u00a0believe\u00a0such\u00a0 is not really the case with respect to asset failure events.<\/em><\/p>\n

The field of Condition Based Maintenance (CBM) would appear to negate the idea of randomness applied to equipment reliability, since its\u00a0premise is that of predictive<\/em> maintenance. How exactly does CBM reduce or reconcile randomness with predictibility? Let us assume that Weibull’s equation for Reliability<\/p>\n

\"R(t)=1-F(t)=e^{-\left ( \frac{t}{\eta } \right )^{\beta }}\"<\/p>\n

where t is age, \u03b2 and \u03b7 are parameters, describes\u00a0the relationship between a part’s (or failure mode’s) age and its reliability, its probability of\u00a0surviving to that age. By years of experience maintenance\u00a0engineers \u00a0know that age alone is usually insufficient information upon which to judge an item’s ability\u00a0to survive the next time interval, say one month or 10000 widgets,\u00a0when asking the question\u00a0at the current moment in time. All maintenance planners and engineers recognize that additional data, called “conditional data varaiables”\u00a0are required, that would indicate the part’s current state of health. But they don’t know how, to incorporate that additional information into the reliability calculation. They don’t even have a systematic\u00a0method by which\u00a0to judge which condition data variables contain predictive content. Nor can they\u00a0evaluate the degree to which condition data would mitigate\u00a0the “randomness” that interferes with practical failure prediction.<\/p>\n

This article will introduce a simple method for systematizing CBM prediction. Instinctively we know that while the age of an item while\u00a0not a determinative factor certainly influences the propensity of a part to fail. We will explore\u00a0the questions: What is the relative influence of age on failure? And, what is the relative influence of each of the other relevant condition variables on failure probability?<\/p>\n\n","protected":false},"excerpt":{"rendered":"

The word “Random” might confuse us\u00a0when we refer to Nowlan and Heap’s\u00a0failure pattern B that describes “age independent failure”. \u00a0When using\u00a0the word random applied to an event, we tend to infer that the event is unpredictable. Randomness suggests a non-coherence, such that there is no intelligible pattern or combination that may be discerned. But maintenance […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"single-with-sidebar","format":"standard","meta":{"footnotes":""},"categories":[89],"tags":[],"class_list":["post-4164","post","type-post","status-publish","format-standard","hentry","category-theory-and-definitions"],"_links":{"self":[{"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/posts\/4164","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/comments?post=4164"}],"version-history":[{"count":1,"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/posts\/4164\/revisions"}],"predecessor-version":[{"id":8720,"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/posts\/4164\/revisions\/8720"}],"wp:attachment":[{"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/media?parent=4164"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/categories?post=4164"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.livingreliability.com\/en\/wp-json\/wp\/v2\/tags?post=4164"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}