What is preven­tive mainten­ance?

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The term “preven­tive mainten­ance” is often used in connec­tion with the optimiza­tion of mainten­ance plans. This article explains what is behind this and how to imple­ment preven­tive mainten­ance based on histo­rical failure data.

A very well-known and old example of preven­tive mainten­ance is the V-belt in a car, which trans­mits the rotation of the engine cranks­haft to various auxiliary units. It is parti­cu­larly important to supply the alter­nator, which provides power to many of the car’s electrical compon­ents, as well as the hydraulic pump, water pump, power steering, venti­la­tion and air condi­tio­ning.

Back to preven­tive mainten­ance: Experi­ence has shown that a V-belt lasts an average of around 100,000 kilome­ters. However, the running time depends on various factors, such as the power of the engine, the age of the V-belt and the number of start/stop opera­tions. Without knowing these influences precisely, a safety factor is applied to the average service life of the V-belt. The safety factor must also take into account that a broken V-belt can lead to signi­fi­cant engine damage. For example, we set the safety factor to γ=0.8 and replace the V-belt appro­xi­m­ately every γ⋅100,000=80,000 kilome­ters, using the inspec­tion dates recom­mended by the manufac­turer. Of course, indivi­dual V-belts can also last 120,000 or 150,000 kilome­ters, but proac­tive repla­ce­ment or preven­tive mainten­ance minimizes the risk of engine damage without comple­tely elimi­na­ting it. When deter­mi­ning the safety factor, the poten­tial engine damage must be taken into account on the one hand; on the other hand, the effort or workshop costs for repla­cing the V-belt.

This example can be applied to all mecha­nical (and electronic) devices. Typically, the expected load cycles of ball bearings, the average burning time of light bulbs or the service life of sealing rings are known. These compon­ents are used in trains, airplanes, genera­tors, robots, eleva­tors, power plants, bicycles, etc. in every country in the world. These many millions of compon­ents make effec­tive preven­tive mainten­ance important in order to avoid damage and system failures.

So how does preven­tive mainten­ance work? The example of the V-belt has already shown this quite well: you need histo­rical failure data for the system type under conside­ra­tion or, as in the example of the V-belt, histo­rical failure data for the compo­nent under conside­ra­tion. If you want to carry out the calcu­la­tion for a new type of system, you can aggre­gate findings from the installed compon­ents or test the system in various practical tests in the labora­tory.

As with V-belts, in practice there are several time dimen­sions that describe the failure inter­vals of the system. Let’s take the example of a car that is rarely driven. In this case, the V-belt will eventually become porous and may only last 50,000 kilome­ters or less. In another example, the car is moved several times a day (e.g. in a postal delivery vehicle), which can also cause the V-belt to break after 50,000 kilome­ters. A distinc­tion is made between the Mean Time Between Removal (MTBR), the Mean Duration Between Removal (MDBR) and the Mean Cycles Between Removal (MCBR). Depen­ding on the appli­ca­tion, further time dimen­sions are conceivable. A specific failure interval results as a linear combi­na­tion of the indivi­dual time dimen­sions, the coeffi­ci­ents of which are derived from an optimiza­tion algorithm. Typically, we use a meta-heuristic such as Simulated Annealing for this task, which converges to a good (local) optimum in finite time.

The next step is to deter­mine which situa­tions can have an influence on the failure of the system/component. To this end, it is advisable to consult experts from develo­p­ment and produc­tion and, if neces­sary, users. If there are too many influences, these can be reduced using statis­tical methods for feature selec­tion. Alter­na­tively, the infor­ma­tion content of the influences can be condensed using a principal compo­nent or factor analysis. In the case of V-belts, these are typically the average speed of the belt, the quality of the material, the ambient tempe­ra­tures (perhaps a V-belt lasts longer in Norway than in Saudi Arabia? or shorter?) and others.

A set of rules must now be derived from the influen­cing varia­bles in order to be able to predict specific failure behavior. Statis­tical decision trees or the Random Forest machine learning method are usually used for this purpose. Both methods arrange the influences in descen­ding order depen­ding on their infor­ma­tion content and separate the charac­te­ristics in each tree level as shown schema­ti­cally in the follo­wing figure. In order to achieve maximum flexi­bi­lity in the conside­ra­tion and combi­na­tion of all influences and time dimen­sions, we have also imple­mented indivi­dual algorithms for clamping the trees for our custo­mers.

Was ist eigentlich Preventive Maintenance?

Figure 2: Schematic repre­sen­ta­tion of a decision tree for the imple­men­ta­tion of preven­tive mainten­ance for V-belts

Each node of the tree contains average times (for three time dimen­sions) from all histo­rical failures that fulfill the criteria of the branch under conside­ra­tion. The tree shown was not fully formed (this is known as prunning) because, for example, only a small amount of data was available from manufac­turer 5. The diffe­rence between the actual distances between two changes and the calcu­lated times in the corre­spon­ding node of the tree deter­mine the quality of the tree.

A typical service life can now be read from the tree for a specific appli­ca­tion situa­tion of the part or compo­nent. As described above, it is advisable to apply a safety factor to the times, which should take into account the costs of failure and the costs of repla­ce­ment. The forecast model should also be presented with current outage and repla­ce­ment infor­ma­tion in order to be able to take changed environ­mental condi­tions into account in the forecast.

To increase the forecast quality, either further statis­tical charac­te­ristics can be taken into account or data from the specific V-belt (the specific serial) can be recorded with the aid of sensors. For example, an acoustic sensor could be used to measure the smooth running of the V-belt. If sensors are used to measure the current condi­tion of the compo­nent, this is referred to as “predic­tive mainten­ance”. How predic­tive mainten­ance works will be explained in one of our next blog posts.

Picture of Björn Piepenburg

Björn Piepen­burg

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