In the field of vehicle durability, many individuals are focusing on methods for better replicating the durability a user will experience throughout the typical design lifespan of a vehicle (e.g., 100,000 miles). To estimate user durability a means of understand the types of damaging events and driving styles of uses must be understood. The difficulty with accurately estimating customer usage is, firstly, there is a large pool of possible roads for a user to drive along, for example, there are over 4 million miles of public roads in the United States, alone [1]. In addition, while measurements of these surfaces could be collected it would be impractical for two reasons, the first is the financial and extreme time burden this would take. Second, when collecting measurements of a road surface only the current state of a road surface can be measured, thus as a road deteriorates or is repaved the measurements collected would no longer be an accurate representation of the road. It should be mentioned that even, if all of the road surfaces were measured performing simulation and analysis of all of these road surfaces would be computationally intensive. Instead, it would be beneficial if select events that account for a significant portion of the damage a vehicle experiences can be identified. These damaging events could then be used in more complex vehicle simulation models and as input and validation of proving ground and laboratory durability testing. The objective of this research is to provide a means for improved estimation of vehicle durability, specifically a means for identifying, characterizing, and grouping unique separable damaging events from a road profile measurement. In order to achieve this objective a measure that can be used to identify separate damaging events from a road profile is developed. This measure is defined as Localized Pseudo Damage (LPD), which identifies the amount of damage each individual road excitation makes to the total accumulated damage for a single load path in a vehicle system. LPD is defined as a damage density to minimize the effect of measurement spacing on the resulting metric. The developed LPD measure is causal in that the value of LPD at a location is not affected by any future locations. In addition, for a singular event (e.g., impulse or step) in the absences of other excitations, the LPD value at the singular event location is equivalent to the total pseudo damage divided by the step size at the location. Once a measure of pseudo damage density is known at multiple locations along a road profile for multiple load paths of interest, then separable damaging events can be identified. To identify separable damaging events the activity of the vehicle system must be considered because separate damaging events can only occur when a region of inactivity is present across all load paths. Subsequently, an optimization problem is formed to determine the optimal active regions to maintain. The cost function associated with the optimization problem is defined to minimize the cost (number of locations maintained in damaging events) and maximize the benefit (the amount of pseudo damage maintained). Lastly, a statistical test is developed to assess if separate damaging events can be considered to be from the same general class of events based on their damage characteristics. The developed assessment methods establish the similarity between two more separable damaging events based on application specific user defined inputs. In the development, two example similarity metrics are defined. The first similarity metric is in terms of distance and the second is in terms of likelihood (probability). The developed statistical analysis uses the current state-of-the-art in clustering algorithms to allow for multiple damaging events to be identified and grouped together. / Doctor of Philosophy / In the automotive field determining the level of damage a typical production vehicle experiences over its lifetime has always been a desirable criterion to identify. This criterion is commonly referred to as customer usage. By understanding the typical customer usage of a vehicle over the lifetime of a vehicle, automotive engineers are able to improve the design of vehicle components. The issue with defining customer usage is that there are millions of miles of roads that a customer can travel on and millions of customers that all have unique driving characteristics. While it is possible to collect measurements of these road surfaces to use in further vehicle simulations, it is not feasible both from a financial and time perspective. In addition, the simulation and analysis of all road surfaces would be computationally intensive. However, if select damaging events (regions of the road surface that excessively contribute to accumulated damage) are identified, then they can be used in more complex vehicle durability analyses with lower computational efforts. In conventional damage analysis a total amount of accumulated damage is established for a known road surface. The issue with defining damage this way is that unique events which likely contributed a large amount of the accumulated damage cannot be identified. The first objective of this research is to define damage as a function of the vehicle's location along a road surface. Then, unique and separable damaging events can be identified and separated from sections of the road that do not significantly contribute to the accumulated damage. After defining this measure, an optimization problem is developed to identify damaging events based on maximizing the benefit (amount of damage accounted for in damaging events) and minimizing the cost (amount of road surface retained). Unique and separable damaging events are identified by solving this optimization problem. While the optimization problem identifies unique, separable damaging events, it is likely that some damaging events contain similar characteristics to each other. When performing additional durability analysis, it would be beneficial to form connections between similar damaging events to allow for analysis to be performed based on groups of events. To identify damaging events with similar characteristics, a statistical analysis is developed as the last contribution of this work. By combining this analysis with current state-of-the-art clustering algorithms and user provided definitions based on applications, similar damaging events are able to be grouped together.

This master thesis studies accelerations recorded and stored onboard Scania trucks. From these accelerations, a general load (𝐹 = 𝑚 ∗ 𝑎) description is deduced. In the next step, an abstract number, ‘pseudo-damage’, representing the load’s fatigue damage potential is calculated. Method At workshop visits, data stored on-board is ‘read out’ and stored in a database. Read-outs from Scania test vehicles are used to study how accumulated pseudo-damage evolves with mileage and test cycles. Pseudo-damage intensity (damage per km or test cycle) variation in carefully controlled vehicle operation, i.e. test track vehicle testing, suggests a threshold between random scatter and variation caused by describable load-influencing factors (vehicle specification, road quality, etc). Influence of wheel configuration – two or three axles – on accelerations and pseudo-damage is studied as an example of vehicle specification influence. Result Test track operation produces considerably higher pseudo-damage intensities than operation on public roads. A ‘normal’ pseudo-damage distribution is shown on title page. But when the vehicle’s operation is split in two distinctly different modes, the distribution displays two peaks instead. RSD, relative standard deviation of pseudo-damage intensity, is used to quantify scatter and variation. Available data indicates that RSD below 0,3 can be regarded as purely random scatter. RSD values above this threshold suggests that traceable, maybe intentional, variation of loadinfluencing factors is present. A pilot study of axle configuration’s influence on loads (pseudo-damage calculated from acceleration matrices) produces inconclusive results, the available database (number of trucks) is too small. Conclusions and recommendations On-board storage of acceleration data provides a general ‘fatigue load’ indication. Resolution is sufficient for meaningful load variation studies. Regular, closer-spaced read-outs would facilitate more detailed studies of test vehicles’ operation. / Mastersarbetet studerar accelerationer, uppmätta och lagrade hos Scanias fordon. Från dessa accelerationer är begreppet ‘pseudo-skada’ beräknat. Pseudo-skada representerar en lasts utmattnings skada potential. Metod Vid verkstadsbesök sparas den data som finns lagrad ombord på lastbilen till en databas. Med utläsningar från testfordonen studeras hur den ackumulerade pseudo-skadan beror på miltal och antal körda test cykler. Genom analys utav variationen i pseudo-skada intensitet (skada per km eller test cykel) från en välkontrollerad fordonsdrift, exempelvis test utförda på test banan, fås ett tröskelvärde mellan slumpmässiga variationer och variationer som beror på förklarliga belastningspåverkande faktorer (fordon specifikation, väg kvalité, etc.) Som ett exempel på influens från olika fordon specifikationer, studeras beroendet mellan pseudoskada och hjulkonfiguration. Fordon med antingen två eller tre hjulaxlar är studerade. Resultat Vid framförande av fordon på testbanan genereras högre pseudo-skada intensitet än när fordonet framförs på allmänna vägar. Figuren på titelsidan visar den normala fördelningen utav pseudo-skada. När fordonsdriften är uppdelad i två distinkta körsätt visar istället fördelningen upp två stycken pikar. Relativ standard avvikelse, RSD, av pseudo-skada intensiteten är beräknad för att kvantifiera slumpmässig och betydelsefull variation. Tillgänglig data visar att ett RSD under 0,3 är att se som slumpmässig variation. RSD värden över detta tröskelvärde visar på existens utav spårbara variationer från belastningspåverkande faktorer. Resultaten från förstudien av beroendet mellan hjulkonfiguration och pseudo-skada är inte slutgiltiga. Den analyserade datamängden är för liten. Slutsatser och rekommendationer De lagrade accelerationerna ger en generell indikation om ‘utmattningslasten‘. Upplösningen är tillräcklig för att kunna studera de meningsfulla lastvariationerna. Regelbundna utläsningar, i ett tätare intervall, skulle främja mer detaljerade studier utav fordonsdriften.