{"id":9406,"date":"2022-12-30T13:22:44","date_gmt":"2022-12-30T20:22:44","guid":{"rendered":"https:\/\/limblecmms.com\/?p=9406"},"modified":"2025-05-30T13:04:18","modified_gmt":"2025-05-30T19:04:18","slug":"failure-patterns","status":"publish","type":"post","link":"https:\/\/limblecmms.com\/blog\/failure-patterns\/","title":{"rendered":"An introduction to equipment failure patterns"},"content":{"rendered":"<div class=\"wpb-content-wrapper\"><p>[vc_row kd_background_image_position=&#8221;vc_row-bg-position-top&#8221;][vc_column][vc_column_text]<span style=\"font-weight: 400;\">Only a small percentage of<\/span><a href=\"https:\/\/limblecmms.com\/blog\/equipment-failure\/\"> <span style=\"font-weight: 400;\">equipment failures<\/span><\/a><span style=\"font-weight: 400;\"> occur due to aging.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Researchers have identified many equipment failure patterns, and have <span id=\"history-of-failure-pattern-studies\"><\/span>calculated the percentage of failures each of them represents.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Understanding these patterns helps reliability and maintenance managers develop<\/span><a href=\"https:\/\/limblecmms.com\/blog\/maintenance-strategy\/\"> <span style=\"font-weight: 400;\">maintenance strategies<\/span><\/a><span style=\"font-weight: 400;\"> that prevent specific failure causes, increasing asset life and reliability while reducing maintenance costs.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">A brief history of failure pattern studies<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Before 1960, maintenance thinking relied heavily on fixed overhauls and defined maintenance periods. However, in-service experience showed little improvement in asset performance despite diligent adherence to the planned maintenance schedules.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In order to improve equipment reliability, many organizations researched failure patterns and causes. The three most notable studies include:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">F. Stanley Nowlan and Howard F. Heap published a report in 1978 called<\/span><a href=\"https:\/\/www.maintenance.org\/fileSendAction\/fcType\/0\/fcOid\/399590942963794131\/filePointer\/399590942964794719\/fodoid\/399590942964794717\/Pages_from_RCMOrig.pdf\"> <span style=\"font-weight: 400;\">AD\/A066 579, Reliability-Centered Maintenance<\/span><\/a><span style=\"font-weight: 400;\">. This document proved to be an influential work that formed the foundation of the maintenance practices used in aviation today.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">In 1992, John Moubray introduced<\/span><a href=\"https:\/\/www.amazon.com\/Reliability-Centered-Maintenance-Second-John-Moubray\/dp\/0831131462\"> <span style=\"font-weight: 400;\">RCM II<\/span><\/a><span style=\"font-weight: 400;\">, which used an almost identical approach to Nowlan and Heap, but focused on establishing safe minimum maintenance levels for other <span id=\"six-common-failure-patterns-explained\"><\/span>industries.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The Naval Sea Systems Command activity, SUBMEPP, completed<\/span><a href=\"http:\/\/www.plant-maintenance.com\/articles\/SubmarineMaintenanceDataRCM.pdf\"> <span style=\"font-weight: 400;\">their study in 1998<\/span><\/a><span style=\"font-weight: 400;\">, generating age and reliability curves from submarine maintenance data. Their findings allowed the Navy to delete many time-directed component overhauls and considerably<\/span><a href=\"https:\/\/limblecmms.com\/blog\/maintenance-cost-explained\/\"> <span style=\"font-weight: 400;\">reduce maintenance costs<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/li>\n<\/ul>\n<p>[\/vc_column_text][\/vc_column][\/vc_row][vc_row content_placement=&#8221;middle&#8221; kd_background_image_position=&#8221;vc_row-bg-position-bottom&#8221; kd_top_separator_style=&#8221;skew-left&#8221; kd_top_separator_height=&#8221;separator-height-small&#8221; kd_bottom_separator_style=&#8221;skew-left&#8221; kd_bottom_separator_height=&#8221;separator-height-small&#8221; kd_top_separator=&#8221;true&#8221; kd_bottom_separator=&#8221;true&#8221; css=&#8221;.vc_custom_1682969636891{margin-bottom: 40px !important;padding-top: 120px !important;padding-right: 40px !important;padding-bottom: 50px !important;padding-left: 40px !important;background: #dde4e8 url(https:\/\/limblecmms.com\/wp-content\/uploads\/cta-laired-hex-4.webp?id=9077) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;border-radius: 0px !important;}&#8221; css_tablet_landscape=&#8221;.vc_custom_1682969636891{padding-bottom: 80px !important;}&#8221; css_tablet_portrait=&#8221;.vc_custom_1682969636891{padding-bottom: 80px !important;}&#8221; css_mobile=&#8221;.vc_custom_1682969636892{padding-bottom: 80px !important;}&#8221;][vc_column][vc_row_inner kd_background_image_position=&#8221;vc_row-bg-position-top&#8221;][vc_column_inner]<header class=\"kd-section-title col-lg-12 text-center  subtitle-below-title kd-animated fadeIn   vc_custom_1687971311556\" data-animation-delay=200><h2 class=\"separator_off\" style=\"font-size: 42px;font-weight: 500;margin-bottom:30px;\">The Essential Guide to CMMS<\/h2><h6 class=\"subtitle\" style=\"color: #152232;\">Download this helpful guide to everything a CMMS has to offer.<\/h6><\/header>[\/vc_column_inner][\/vc_row_inner][vc_row_inner content_placement=&#8221;top&#8221; kd_background_image_position=&#8221;vc_row-bg-position-top&#8221;][vc_column_inner width=&#8221;1\/2&#8243; css=&#8221;.vc_custom_1631866454223{padding-right: 50px !important;padding-left: 0px !important;}&#8221; offset=&#8221;vc_col-lg-6 vc_col-md-12 vc_col-xs-12&#8243; css_tablet_landscape=&#8221;.vc_custom_1631866454223{padding-right: 0px !important;}&#8221; css_tablet_portrait=&#8221;.vc_custom_1631866454223{padding-right: 0px !important;}&#8221; css_mobile=&#8221;.vc_custom_1631866454224{padding-right: 15px !important;}&#8221;][vc_raw_html]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[\/vc_raw_html][\/vc_column_inner][vc_column_inner width=&#8221;1\/2&#8243;][vc_single_image source=&#8221;external_link&#8221; alignment=&#8221;center&#8221; css_animation=&#8221;fadeIn&#8221; custom_src=&#8221;https:\/\/3975608.fs1.hubspotusercontent-na1.net\/hubfs\/3975608\/Content%20Downloads\/CMMS%20Guide%20mockup.png&#8221;][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row kd_background_image_position=&#8221;vc_row-bg-position-top&#8221;][vc_column][vc_column_text]<\/p>\n<h2><span style=\"font-weight: 400;\">Six common failure patterns explained<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The following six unique patterns reported in the findings of failure pattern researchers Nowlan &amp; Heap continue to be validated through studies today.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In the following graphs:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">the Y-axis shows the conditional probability of failure<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">the X-axis shows the operating age of the component<\/span><\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">Failure Pattern A: The Bathtub Curve<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The bathtub curve is the most recognized failure pattern.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Component populations that exhibit bathtub failures show a high failure rate at the start of their operating life, known as <\/span><b>infant mortality<\/b><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">These failures decrease and stabilize for most of the component&#8217;s useful life. The failure probability increases toward the end of the lifespan, known as the <\/span><b>wear-out zone<\/b><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p>\n<p><a href=\"https:\/\/limblecmms.com\/wp-content\/uploads\/The-Bathtub-Curve.webp\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-9407\" src=\"https:\/\/limblecmms.com\/wp-content\/uploads\/The-Bathtub-Curve.webp\" alt=\"\" width=\"2521\" height=\"1020\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">The challenge for maintenance providers is battling through the heightened chance of initial failure before settling into a consistently low random failure probability. We must then change the component out before entering the wear-out zone.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In the 1960s study on civil aviation, approximately 4% of components conformed to the bathtub curve failure pattern. A Swedish study in Broberg in 1973 calculated 3%, and the SUBMEPP study in 1993 reported 6%.<\/span><\/p>\n<p><b>Examples of components that exhibit bathtub failure pattern:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Electrical components like computer hard-disk drives or current relays; while early failures can occur, once burnt in, they will run at low random failures until they wear out from age and use.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Sleeve bearings in large diesel engines and ship propeller shafts. Early failures occur due to bedding-in problems and\/or defects in material or workmanship. Those that pass this stage will run for long periods without significant failures. The failures that do occur will be due to oil quality issues, rotor problems, etc. Eventually, the bearing metal will wear out.<\/span><\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">Failure Pattern B: The Wear-Out Curve<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The wear-out curve shows a stable and low level of random failures for most of its life before sharply increasing toward the end of its<\/span><a href=\"https:\/\/limblecmms.com\/blog\/useful-life-of-asset\/\"> <span style=\"font-weight: 400;\">useful life<\/span><\/a><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In effect, this is the bathtub curve without the infant mortality phase. This lack of infant mortality might be due to the component type or a rigorous supplier quality system that removes all components with early failure.\u00a0<\/span><\/p>\n<p><a href=\"https:\/\/limblecmms.com\/wp-content\/uploads\/The-Wear-Out-Curve.webp\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-9408\" src=\"https:\/\/limblecmms.com\/wp-content\/uploads\/The-Wear-Out-Curve.webp\" alt=\"\" width=\"2521\" height=\"1020\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Replacing the component before entering the wear-out phase is a good way to improve <\/span><a href=\"https:\/\/limblecmms.com\/blog\/equipment-reliability\/\"><span style=\"font-weight: 400;\">equipment reliability<\/span><\/a><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Nowlan &amp; Heap&#8217;s UAL study reported 2% of components showed the wear-out failure pattern; Broberg\u2019s 1%, and SUBMEPP\u2019s a huge 17%.<\/span><\/p>\n<p><b>Examples of components that follow the wear-out failure pattern:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Phosphor bronze or white metal bushes are classic examples of components subject to wear out. They will operate well if lubricated and maintained, but as they age, the shaft clearances increase, accelerating wear until vibration or leakage occurs.<\/span><\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">Failure Pattern C: The Fatigue Curve<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The fatigue curve shows a constant increase in the probability of failure as the component ages.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">There is no defined break-point before which you can plan replacement. Therefore, your decision to replace the item occurs once the failure probability reaches an unacceptable level for your business.<\/span><\/p>\n<p><a href=\"https:\/\/limblecmms.com\/wp-content\/uploads\/The-Fatigue-Curve.webp\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-9409\" src=\"https:\/\/limblecmms.com\/wp-content\/uploads\/The-Fatigue-Curve.webp\" alt=\"\" width=\"2521\" height=\"1020\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">This pattern covers 5% of all failures in the UAL study, 4% in Broberg, and 0% from SUBMEPP.<\/span><\/p>\n<p><b>Examples of components that exhibit the fatigue failure pattern:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Fatigue failure is common in components subjected to cyclical loads, like gear teeth, springs, or drive shafts.<\/span><\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">Failure Pattern D: The Break-In Curve<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The break-in curve has a very low probability of failure at the beginning of the component&#8217;s life.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The probability increases early, settling at a constant conditional probability for the remainder of the component&#8217;s life.<\/span><\/p>\n<p><a href=\"https:\/\/limblecmms.com\/wp-content\/uploads\/The-Break-In-Curve.webp\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-9410\" src=\"https:\/\/limblecmms.com\/wp-content\/uploads\/The-Break-In-Curve.webp\" alt=\"\" width=\"2521\" height=\"1020\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">There is no value in replacing this component to improve reliability, as the initial rise in failures forms a small percentage of the overall useful life of the component.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This break-in curve covered 7% of all failures at UAL, 11% in Sweden, and 0% in the submarine service.<\/span><\/p>\n<p><b>Examples of components that exhibit the break-in failure pattern:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A capacitance level sensor is an example of a component exhibiting the break-in curve. Given the role of the component, the manufacturer captures manufacturing defects before shipping. The failure probability of the population increases as each item is subject to in-service environmental factors before exhibiting a stable random failure mode.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Can also be seen in various electrical components.<\/span><\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">Failure Pattern E: The Random Pattern<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The failure probability for this component is constant, meaning it has a random failure distribution, without increases or decreases,<\/span><a href=\"https:\/\/limblecmms.com\/maintenance-definitions\/asset-lifecycle-management\/\"> <span style=\"font-weight: 400;\">throughout its lifecycle<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><a href=\"https:\/\/limblecmms.com\/wp-content\/uploads\/The-Random-Pattern.webp\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-9411\" src=\"https:\/\/limblecmms.com\/wp-content\/uploads\/The-Random-Pattern.webp\" alt=\"\" width=\"2521\" height=\"1020\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">No optimum time exists to change this component, as doing so will not affect reliability.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This pattern accounted for 14% of all UAL failures, 15% at Broberg, and 42% at SUBMEPP.<\/span><\/p>\n<p><b>Examples of components that exhibit the random failure pattern:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">This pattern is rare on mechanical components, which are more likely to exhibit wear out, and on many electronic components, which exhibit infant mortality. However, solid-state memory chips exhibit a constant failure rate due to an implicit self-correction mechanism.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Random failures can happen due to a seemingly infinite number of reasons \u2014 tires punctured by nails, pump suctioning in debris, operator errors, etc. When you aggregate those on a scale large enough, you can get a constant failure rate.<\/span><\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">Failure Pattern F: The Infant Mortality Curve<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">This curve mirrors the start of the bathtub curve, exhibiting high failure probability in the early stages of life before settling into a steady random failure probability for the remainder of the component&#8217;s life.<\/span><\/p>\n<p><a href=\"https:\/\/limblecmms.com\/wp-content\/uploads\/The-Infant-Mortality-Curve.webp\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-9412\" src=\"https:\/\/limblecmms.com\/wp-content\/uploads\/The-Infant-Mortality-Curve.webp\" alt=\"\" width=\"2521\" height=\"1020\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Changing this component doesn&#8217;t only provide zero benefits, it actually increases the failure probability by introducing a new infant mortality phase.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This failure pattern accounts for 68% of all Nowlan &amp; Heap&#8217;s components, 66% of the Swedes, and 29% of the submarine service.<\/span><\/p>\n<p><b>Examples of components that exhibit the infant mortality failure pattern:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">In general, this curve applies to complex equipment without <span id=\"what-failure-patterns-tell-us\"><\/span>dominant<\/span><a href=\"https:\/\/limblecmms.com\/blog\/fmea-and-fmeca\/\"> <span style=\"font-weight: 400;\">failure modes<\/span><\/a><span style=\"font-weight: 400;\">. Even though each individual component might follow Patterns A through D, when assembled together, the machine will exhibit a constant failure rate after an initial bedding-in period.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">CPUs and video processing chips are examples of components that exhibit infant mortality before settling into a constant low-level failure rate for the rest of their lives.<\/span><\/li>\n<\/ul>\n<p>[\/vc_column_text][\/vc_column][\/vc_row][vc_row content_placement=&#8221;middle&#8221; kd_background_image_position=&#8221;vc_row-bg-position-center&#8221; kd_top_separator_style=&#8221;arrow-down&#8221; kd_top_separator_height=&#8221;separator-height-small&#8221; kd_bottom_separator_style=&#8221;arrow-down&#8221; kd_bottom_separator_height=&#8221;separator-height-small&#8221; kd_top_separator=&#8221;true&#8221; kd_bottom_separator=&#8221;true&#8221; css=&#8221;.vc_custom_1682969283095{margin-bottom: 40px !important;padding-top: 60px !important;padding-bottom: 40px !important;background: #dde4e8 url(https:\/\/limblecmms.com\/wp-content\/uploads\/cta-laired-hex-4.webp?id=9077) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;border-radius: 0px !important;}&#8221; css_tablet_landscape=&#8221;.vc_custom_1682969283096{padding-bottom: 80px !important;}&#8221; css_tablet_portrait=&#8221;.vc_custom_1682969283096{padding-bottom: 80px !important;}&#8221; css_mobile=&#8221;.vc_custom_1682969283097{padding-bottom: 80px !important;}&#8221;][vc_column][vc_row_inner content_placement=&#8221;middle&#8221; gap=&#8221;15&#8243; kd_background_image_position=&#8221;vc_row-bg-position-top&#8221;][vc_column_inner width=&#8221;1\/2&#8243;]<header class=\"kd-section-title col-lg-12 text-left  subtitle-below-title kd-animated fadeIn   vc_custom_1682969314293\" data-animation-delay=200><h2 class=\"separator_off\" style=\"font-size: 42px;font-weight: 500;margin-bottom:30px;\">Mean Time Metrics Calculator<\/h2><h6 class=\"subtitle\" style=\"color: #152232;\">Just getting started with maintenance metrics? Use this helpful calculator with formulas and calculations.<\/h6><\/header>[\/vc_column_inner][vc_column_inner width=&#8221;1\/2&#8243;][vc_raw_html]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[\/vc_raw_html][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row][vc_row kd_background_image_position=&#8221;vc_row-bg-position-top&#8221;][vc_column][vc_column_text]<\/p>\n<h2><span style=\"font-weight: 400;\">What failure patterns tell us<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">While the percentages vary from study to study, we can take some important guidance from these patterns and their implications.<\/span><\/p>\n<p><a href=\"https:\/\/limblecmms.com\/wp-content\/uploads\/equipment-failure-pattern-scaled-1.webp\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-9413\" src=\"https:\/\/limblecmms.com\/wp-content\/uploads\/equipment-failure-pattern-scaled-1.webp\" alt=\"\" width=\"2560\" height=\"1195\" \/><\/a><\/p>\n<h3><span style=\"font-weight: 400;\">Age-related failures<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Patterns A through C are examples of age-related failures where we can define a component\u2019s life. The failure probability will increase once the part reaches a certain age, hours, or cycles in operation.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Wear or corrosion are examples of <\/span><a href=\"https:\/\/limblecmms.com\/blog\/equipment-failure\/\"><span style=\"font-weight: 400;\">failure causes<\/span><\/a><span style=\"font-weight: 400;\">. Based on the percentages identified for each curve, only approximately 15% of all components would benefit from having a defined life, providing increased equipment reliability.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Random failures<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Patterns D through F have a short period of higher failure probability when new, before settling into a period of low random failures.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">While this doesn&#8217;t mean that some of these components wouldn&#8217;t benefit<span id=\"tools-and-strategies-for-equipment-failure\"><\/span> from <\/span><a href=\"https:\/\/limblecmms.com\/strategies\/preventive-maintenance\/\"><span style=\"font-weight: 400;\">preventive maintenance<\/span><\/a><span style=\"font-weight: 400;\">, we will only see small to moderate gains in reliability for an increased cost and administrative burden.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Approximately 85% of all components fall into the random failure category. That is a huge percentage, so it isn\u2019t surprising that we have developed efficient ways to address this problem.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Tools and strategies for handling random equipment failures<\/span><\/h2>\n<p><b>The fact the failures are random doesn&#8217;t mean there are no signs of imminent failure<\/b><span style=\"font-weight: 400;\">. Here are some tools and strategies that can help you catch deterioration signs early and <\/span><a href=\"https:\/\/limblecmms.com\/blog\/pf-curve-and-pf-interval\/\"><span style=\"font-weight: 400;\">extend your P-F interval<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Condition-based maintenance<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Nowlan &amp; Heap found the solution to managing random failure components lies in <\/span><a href=\"https:\/\/limblecmms.com\/strategies\/condition-based-maintenance\/\"><span style=\"font-weight: 400;\">condition-based maintenance (CBM)<\/span><\/a><span style=\"font-weight: 400;\">. The CBM philosophy relies on replacing components when visual inspections or remote monitoring suggest that a component is approaching a point of failure.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Currently, most CBM initiatives use sensors connected to critical components to collect real-time data. Computers watch for any changes in the received signals that may indicate component deterioration or potential failure.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Predictive maintenance<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Combining condition monitoring with machine learning leads to<\/span><a href=\"https:\/\/limblecmms.com\/strategies\/predictive-maintenance\/\"> <span style=\"font-weight: 400;\">predictive maintenance<\/span><\/a><span style=\"font-weight: 400;\">, where an analytics engine assesses all possible failure modes and identifies how long the component will continue to operate.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This prediction is sent to maintenance planners to ensure that the planned maintenance intervention, along with spare part allocations, occur right before component failure.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Condition monitoring includes techniques like<\/span><a href=\"https:\/\/limblecmms.com\/blog\/oil-analysis\/\"> <span style=\"font-weight: 400;\">oil analysis<\/span><\/a><span style=\"font-weight: 400;\">,<\/span><a href=\"https:\/\/limblecmms.com\/blog\/vibration-analysis\/\"> <span style=\"font-weight: 400;\">vibration analysis<\/span><\/a><span style=\"font-weight: 400;\">, sound, temperature, pressure, or electrical current monitoring.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Computerized Maintenance Management Software\u00a0<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Condition-based maintenance requires consistent data, reliability expertise, and equipment failure analysis \u2014 all combined to allow a targeted application of appropriate maintenance strategies.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Most organizations use <\/span><a href=\"https:\/\/limblecmms.com\/cmms\/\"><span style=\"font-weight: 400;\">CMMS<\/span><\/a><span style=\"font-weight: 400;\"> as a key enabler of condition-based maintenance, enabling data collection, storage, and the real-time monitoring of critical components.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A modern cloud-based CMMS provides processing power and data security for the large data volumes created by CBM. It provides insight into trending conditions, and helps predict the time until failure \u2014 well before human monitoring can detect a change.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">While implementing a CMMS and CBM system requires an investment in sensors and software, the savings generated by cutting down on excessive maintenance and improving productivity pay back the initial <span id=\"key-takeaways\"><\/span>investment rapidly.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">However, the benefit to the organization is not reflected solely in maintenance savings. Equipment reliability improves, increasing uptime and<\/span><a href=\"https:\/\/limblecmms.com\/blog\/useful-life-of-asset\/\"> <span style=\"font-weight: 400;\">lengthening the useful life of your equipment<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Key takeaways<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Identifying component failure patterns in the 1960s was a pivotal moment in<\/span><a href=\"https:\/\/limblecmms.com\/blog\/equipment-maintenance\/\"> <span style=\"font-weight: 400;\">equipment maintenance<\/span><\/a><span style=\"font-weight: 400;\">. Subsequent studies have repeatedly supported the conclusion that at least three-quarters of all failures will not see reliability improvements from defined overhaul or inspection periods.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Understanding the implication of failure patterns is an important prerequisite for building an effective maintenance program. Such a program minimizes your organization&#8217;s maintenance costs and effort while maximizing asset life and reliability.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">To learn more about equipment maintenance and reliability, keep browsing the<\/span><a href=\"https:\/\/limblecmms.com\/blog\/\"> <span style=\"font-weight: 400;\">Limble blog<\/span><\/a><span style=\"font-weight: 400;\">.<\/span>[\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>[vc_row kd_background_image_position=&#8221;vc_row-bg-position-top&#8221;][vc_column][vc_column_text]Only a small percentage of equipment failures occur due [&hellip;]<\/p>\n","protected":false},"author":24,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[740],"tags":[],"class_list":["post-9406","post","type-post","status-publish","format-standard","hentry","category-metrics"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.7.1 (Yoast SEO v25.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>An introduction to equipment failure patterns<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/limblecmms.com\/learn\/metrics\/failure-patterns\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"An introduction to equipment failure patterns\" \/>\n<meta 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