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Software systems on electronically controlled diesel truck engines typically provide diagnostic features to enable the engine mechanic to identify and debug system problems. As future systems become more sophisticated, so will the diagnostic requirements. The advantages of serviceability and accuracy found in todays electronic systems must not be allowed to degrade due to this increased sophistication. One method of maintaining a high level of serviceability and accuracy is to place an even greater priority on diagnostics and servicing in the initial design phase of the product than is done today. In particular, three major goals of future diagnostic systems should be separation of component failures from system failures, prognostication of failures and analysis of engine performance. This paper will discuss a system to realize these goals by dividing the diagnostic task into the Electronic System Diagnostics, Engine System Diagnostics and the Diagnostic Interface.

The application of Artificial Intelligence (AI) in the automotive industry can dramatically reshape the industry. In past decades, many Original Equipment Manufacturers (OEMs) applied neural network and pattern recognition technologies to powertrain calibration, emission prediction and virtual sensor development. The AI application is mostly focused on reducing product development and validation cost. AI technologies in these applications demonstrate certain cost-saving benefits, but are far from disruptive. A disruptive impact can be realized when AI applications finally bring cost-saving benefits directly to end users (e.g., automation of a vehicle or machine operation could dramatically improve the efficiency). However, there is still a gap between current technologies and those that can fully give a vehicle or machine intelligence, including reasoning, knowledge, planning and self-learning.

There has been an ongoing interest in replacing mineral oil with more biodegradable and/or fire-resistant hydraulic fluids in many mobile equipment applications. Although many alternative fluids may be more biodegradable, or fire-resistant, or both than mineral oil, they often suffer from other limitations such as poorer wear, oxidative stability, and yellow metal corrosion which inhibit their performance in high-pressure hydraulic systems, particularly high pressure piston pump applications. From the fluid supplier's viewpoint, the development of a definitive test, or series of tests, that provides sufficient information to determine how a given fluid would perform with various hydraulic components would be of interest because it would minimize extensive testing. This is often too slow or prohibitively expensive. Furthermore, from OEM's (original equipment manufacturer's) point of view, it would be advantageous to develop a more effective, industry accepted fluid analysis screening.

Most of the history of systems engineering has been focused on processes for engineering a single complex system. However, most large enterprises design, manufacture, operate, sell, or support not one product but multiple product lines of related but varying systems. They seek to optimize time to market, costs of development and production, leverage of intellectual assets, best use of talented human resources, overall competitiveness, overall profitability and productivity. Optimizing globally across multiple product lines does not follow from treating each system family member as an independently engineered system or product. Traditional systems engineering principles can be generalized to apply to families. This article includes a multi-year case study of the actual use of a generic model-based systems engineering methodology for families, Systematica™, across the embedded electronic systems products of one of the world's largest manufacturers of heavy equipment.

To improve overall customer experience, it is imperative to minimize the noise levels inside agricultural equipment cab. Up-front prediction of acoustic performance in product development is critical to implement the noise control strategies optimally. This paper discusses the methodology used for virtual modeling of a cab on agricultural equipment for prediction of interior noise. The Statistical Energy Analysis (SEA) approach is suitable to predict high frequency interior noise and sound quality parameters such as articulation index and loudness. The cab SEA model is developed using a commercial software. The structural and acoustic excitations are measured through physical testing in various operating conditions. The interior noise levels predicted by the virtual model are compared with the operator ear noise levels measured in the test unit. The resultant SPL spectrum from SEA correlates well with the test.

Pass-by/exterior noise of earth moving machines (EMM) and forestry machines is becoming a focus at early product development stages. ISO 6395 (2) or EC/2000/14 (1) standards defines exterior noise test procedure for EMM. However, these standards do not provide insights for diagnosing any noise issues which may arise. The analysis challenges are posed by the moving machine and acoustic sources with respect to the stationary hemisphere target microphone on the ground and changing operating condition of sources as function of time. There is need to develop a seamless methodology to identify acoustic sources, quantify respective source strengths and rank partial contributions from each source to the total target microphone response in order to overcome the aforementioned challenges.

Faced with competitive environments, pressure to lower development costs and aggressive timelines engineers are not only increasingly adopting numerical simulation techniques but are also embracing design optimization schemes to augment their efforts. These techniques not only provide more understanding of the trade-offs but are also capable of proactively guiding the decision making process. However, design optimization and exploration tools have struggled to find complete acceptance and are typically underutilized in many applications; especially in situations where the algorithms have to compete with existing swift decision making processes. In this paper we demonstrate how the type of setup and algorithmic choice can have an influence and make optimization more lucrative in a new product development atmosphere. We also present some results from a design exploration activity, involving linkage and structural development, of an earth moving machine application.

The induction hardening process involves a complex interaction of electromagnetic heating, rapid cooling, metallurgical phase transformations, and mechanical behavior. Many factors including induction coil design, power, frequency, scanning velocity, workpiece geometry, material chemistry, and quench severity determine a process outcome. This paper demonstrates an effective application of a numerical analysis tool for understanding of induction hardening. First, an overview of the Caterpillar induction simulation tool is briefly discussed. Then, several important features of the model development are examined. Finally, two examples illustrating the use of the computer simulation tool for solving induction-hardening problems related to cracking and distortion are presented. These examples demonstrate the tool's ability to simulate changes in process parameters and latitude of modeling steel or cast iron.

Electronics technology has evolved significantly since the first electronically controlled heavy duty on-highway truck engines were introduced in the mid 1980's. Engine control hardware, software, and sensor designs have been driven by many factors. Emissions regulations, fuel economy, engine performance, operator features, fleet management information, diagnostics, vehicle integration, reliability, and new electronics technology are some of those factors. The latest engine electronics technology is not only found in heavy duty on-highway trucks, but in off-highway applications as well. Track-type tractors, haul trucks, wheel loaders, and agricultural tractors now benefit from the advantages of electronic engines. And, many more new applications are being developed.

This paper presents a virtual analysis method for pressure spike estimation and optimization of hydraulic system architecture for off-highway applications with hydraulically actuated clutch. This pressure spike leads to a very high torque spike in driveline components during clutch pop-up conditions in puddling operations. These torque spikes lead to potential failure of driveline components i.e., gear, shaft, bearing and torsional damper during sudden engagement events. To assess the hydraulic system performance during clutch pop-up cornering conditions is very challenging and leads to compromise on operator safety in the paddy field. It is essential to develop a simulation methodology in a virtual environment to understand the system behavior during clutch pop-up condition and impact of various hydraulic system parameters. This paper describes a Model Based System Design (MBSD) approach for understanding hydraulic system pressure spike phenomenon and dynamic response.

Most pieces of mobile equipment (machines) produce an audible signal to indicate movement in the rearward direction. This signal is intended to alert nearby personnel of the potential danger associated with the machine moving in a direction where the operator may not be able to see people or objects in the machine path. Anyone who has been on or near a construction site recognizes the familiar “beep…beep…beep…” of this signal as the backup alarm. To be effective, the backup alarm must be discernible, timely, and relevant to those people where a reaction is intended. As machine designers respond to various sound directives for reducing sound emissions (including the backup alarm), the performance of the backup alarm is receiving special attention. An emerging solution is an alarm capable of sensing ambient sounds and producing an audible signal proportional to the sensed sound levels-a self-adjusting backup alarm.

Despite numerous research efforts, there is no reliable and widely accepted tool for the prediction of erosion prone material surfaces due to collapse of cavitation bubbles. In the present paper an Erosion Aggressiveness Index (EAI) is proposed, based on the pressure loads which develop on the material surface and the material yield stress. EAI depends on parameters of the liquid quality and includes the fourth power of the maximum bubble radius and the bubble size number density distribution. Both the newly proposed EAI and the Cavitation Aggressiveness Index (CAI), which has been previously proposed by the authors based on the total derivative of pressure at locations of bubble collapse (DP/Dt>0, Dα/Dt<0), are computed for a cavitating flow orifice, for which experimental and numerical results on material erosion have been published. The predicted surface area prone to cavitation damage, as shown by the CAI and EAI indexes, is correlated with the experiments.

Virtual Product Development (VPD) is a key enabler in CAE and depends upon accurate implementation of multibody dynamics. This paper discusses the formulation and implementation of a large-scale multibody dynamics simulation code. In the presented formulation, the joint variables are used as the generalized coordinates and spatial algebra is used to formulate the system equations of motion. Although the presented formulation utilizes the joint variables as the generalized coordinates, closed-loop mechanisms can be easily modeled using impeded constraints. Baumgart stabilization approach is used to eliminate the constraint violations without using the expensive Newton-Raphson iterations. Integrated rigid and flexible body dynamic simulation allows accurate prediction of structural loads, stress, and strains. Both modal and nodal flexible body approaches are discussed in the paper.