buses degradation based on exploration conditions - wseas

Proceedings of the 3rd WSEAS Int. Conf. on ENERGY PLANNING, ENERGY SAVING, ENVIRONMENTAL EDUCATION

BUSES DEGRADATION BASED ON EXPLORATION CONDITIONS ANTÓNIO SIMÕES1; TORRES FARINHA2; INÁCIO FONSECA3; F. MACIEL BARBOSA4; VIRIATO MARQUES5 1,2,3,5 Instituto Superior de Engenharia / Instituto Politécnico de Coimbra; 3Faculdade de Engenharia / Universidade do Porto 1,2,3,5 Rua Pedro Nunes / 3030-199 COIMBRA; 4Rua Dr. Roberto Frias 4200-465 PORTO 1,2,3,4,5 PORTUGAL 1 2 [email protected]; [email protected]; [email protected]; [email protected]; [email protected] Abstract: - In a world with a global market that is continually increasing, the productivity management plays an important key role to keep the competitiveness. The productivity can be achieved through the availability that can be increased through the adoption of efficient maintenance practices, by focusing the organisations on adequate type of maintenance strategy. Condition Based Maintenance (CBM) uses primarily non destructive testing techniques, as environmental indicators, visual inspection, monitoring functioning variables and performance data. This kind of maintenance replaces tasks of scheduled maintenance by warranted interventions predicted by on-condition limits previously defined. The interventions occur only when the equipments presents abnormal symptoms. The intervals of CBM are determined based on the expected delay to fail and are managed through the frequency by which the predictive interventions have to be done. The technological advances are accepted and applied on CBM systems, which includes improved knowledge of failure mechanisms, advancements in, failure forecasting techniques, monitoring and sensor devices, diagnostic and prognostic software, communication protocols, and computer networking technologies. The measurement precision and sensitivity of the Condition Monitoring (CM) techniques are used because they affect the reaction time available to reduce or eliminate the consequences of the functional failure. CBM maintenance tasks must be applicable under the situation of cost effective improvement, including external costs. The base of development of this paper is supported by an integrated system called SMIT (Terology Integrated Modular System), that permits to manage the maintenance itself, including on-condition maintenance, but it is modular enough to permit to associate new features, namely this last approach, in order to maintain the system up-to-date and, always possible, to anticipate the future. Key-Words: - Maintenance; predictive; environmental; degradation; exploration; terology; HMM applications due to lost production, cost of spare parts replacement, quality production deficiencies, and so on. Predictive maintenance through Condition Based Maintenance (CBM) [1] differs from scheduled maintenance because it predicts interventions based on the equipment condition rather than on some preset schedule planning. In the first situation the interventions are made only if some variable had reached some limit and, in second case, the interventions are made based on a systematic planning, even the equipment do not need. Scheduled maintenance is time-based or control functioning variable based, like functioning hours or kilometres. The check list of procedures implies activities such as observing some waste points or used parts changing. For example, in combustion engines, according to scheduled manufacturer planned maintenance, the interval between oil changes usually varies between

1 Introduction Today, the performance monitoring is a relevant aspect to characterize the system state and is an oncondition technique that helps to predict problems by detecting changes on the variables that are correlated to the equipment functioning state. It uses indicators, such as, environmental parameters, pressure, temperature, flow rate, and electrical power consumption, among others. On-condition maintenance objective is to cover all possible failure modes using data of equipment previously collected. Scheduled maintenance is based on definition of intervention intervals using some functioning variable that may imply non-periodic time intervals, in order to prevent excessive wear of components, sub-systems or systems. Non-planned maintenance is performed after an obvious fault or breakdown occurrence. Both approaches have shown to be costly in many

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5.000 and 60.000 Km. Different approach is made in vehicles which maintenance evaluation is planned through CBM. In this case, it is picked up an oil sample with some periodicity, that is analyzed and the results are introduced in a computer program that predicts the time of next maintenance intervention. This is one area where the CBM has achieved more developing. A bus can extent the interval between oil changes until 100.000 Km. This example shows the fundamental difference between predictive maintenance and scheduled maintenance but, however, both were considered planned maintenance methodologies. CBM or predictive maintenance is one mean of improving environmental conditions, productivity, product or service quality and overall effectiveness of services exploitation. CBM is based on effluents monitoring, sound monitoring, thermal imaging, lubricating oil analysis or any other nondestructive testing techniques. It is a philosophy or attitude that simply uses the actual operating condition of equipment and systems to optimize fleet or plant operation. Our goal is to evaluate the environmental degradation of an urban bus fleet, based on exploration conditions and, at same time, based on these conditions, to implement an adequate predictive maintenance to answer those environmental worries. Each route is composed by several arches or line sections - an arch length is a portion of a publictransport line between two nodes. It is recommended to consider nodes where buses have high probability to stop, like traffic lights.

2 Dysfunctions prediction in Bus Fleet Particulate Matter (PM) and opacity are important properties of exhaust gas to measure, once the soot has been implicated as a carcinogen. In addition, PM has been related as an initiator of asthma and respiratory illness. It is hypothesized that soot absorbs small molecules, which upon uptake by the body, releases these toxic substances into the bloodstream. Beyond opacity measurements, this paper makes the integration of gaseous effluents and noise. The challenge is to predict the buses degradation and consequently the vehicle state, knowing the environmental impacts and, at same time, to develop degradation models to support the prediction. Based on the knowledge of transporters, concessionaires and repairers, it is possible to predict dysfunctions on Diesel bus engines and their respective frequency of occurrence.

REPAIR TYPE Air-Fuel Ratio Control Air Filter EGR System Injector Pump Settings (Adjustments) Injector Pump Parts Adjust Rack Adjust Fuel Delay (Injection Time) Fuel Delay Parts Repair Injectors Injector Parts Cylinder Head Overhaul Adjust Valves Turbocharger and Wastegate Valve Cooling System Lubricating System Engine Overhaul Engine Rebuild Other Intake dysfunctions

PERCENTAGE OF DISFUNTIONS 1,2 11,9 6,8 14,8 4,5 1,8 3,9 0,9 11,2 8,6 8,3 6,1 4,3 6,7 1,6 3,7 1,9 1,8

Table 1 - Emissions Related Repair Incidence Generally speaking, dysfunctions in the air intake system and in the fuel system are the most common causes. However, an engine in very poor mechanical condition presents loss of lubricating oil or compression that is the cause that provokes high smoke and gaseous emissions. These typical faults that occur in diesel buses lead to high smoke or gaseous emissions. Impact estimation on air quality is obtained by combining the frequency of occurrence and the

Fig. 1 – Route comprised in the study Yellow: Going route; Green: Return route

With the objective to simplify the study, the effects of street types and driving patterns were incorporated in two main correlated variables: slope of line section and respective stops by class of slope.

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of functional failure is defined as the P-F interval (figure 3). During this warning period, other CBM tasks can be used to diagnose the fault. Globally, the overall failure rate is determined having the assumption that each dysfunction type occurs independently of any other. The P-F interval manages the frequency by which the predictive task must be done. The checking interval must be significantly less than the P-F interval if we wish to detect the potential failure before it becomes a functional failure. The P-F interval can be measured by some unit related to exposure to stress, as running time, stop-start cycles, or others. But, usually, it is measured in terms of elapsed time; for different equipments and failure modes, the P-F interval can vary from fractions of a second to several decades.

emissions rates. Several models of traffic emissions have being developed; one of them is showed in [11]. The emissions rates are a combination of two factors: The emission rate per brake-power per hour (kg/(kW.h); and the engine efficiency in operation ((kW.h)/km). The multiplication of these two factors provides the emissions rate per unit of distance (kg/km). [7] Shows models with factors use-based and estimates the change in emissions from a new state until a determined state, when the vehicle has an excellent maintenance. The model presented in this paper can predict the next dysfunction, independently of age. The program [4] refers that the failure rate in the first five years is very low in relation to the average, presumably because the warranty coverage and improved maintenance by first owners. Failure rate increase rapidly within the interval of 8 to 10 years of age, but only increases a little for buses over 14 to 15 years of age, presumably because the elimination of the engines with greatest use or poorest condition. The resulting failure rate curve is sigmoid shaped (sshaped) as a function of age, where an exponential function was derived to fit the data. The study refer that the peak failure rate varies by model year group, because newer engines with electronic controls have fewer dysfunctions when compared to older engines with mechanical controls. Based on CBM, the selection of condition monitoring techniques and the prediction of interval to fail are crucial. This time can be seen in P-F curve (figure 2), which name appears because it shows how a failure starts and the deterioration develops since the points “P” at which it is detected until the functional failure, point “F”.

Fig. 3 - Interval to fail (P-F interval) The amount of time needed for these answers also varies, from intervals of minutes, hours, until weeks or even months. Unless there is a good reason to do otherwise, it is usually enough to select a checking interval equal to half of P-F interval. However, sometimes it is necessary to select an inspection interval which is some other fraction of P-F interval. Respecting the more critical pollutant, figure 3 shows how a P-F interval of 8 weeks and a checking interval of 2 weeks give a remaining P-F interval of 6 weeks, when the detection occurred almost 2 weeks after the beginning of the dysfunction. Sometimes, this is known as the available P-F interval. The amount of time needed to answer to any potential discovered failure influences on-condition-based inspection intervals. Condition-monitoring maintenance inspection interval must be determined based on the expected PF interval. Because some organizations have detailed knowledge about the equipment failure mode, P-F interval can be analyzed with the objective to optimize the criteria of functional failure, involving different items, like the followings: 1. Expert opinion and back experiences (e.g., manufacturer’s specifications);

Fig. 2 - P-F curve If a potential failure is detected between points P and F, it may be possible to take action to prevent the functional failure (or at least to minimize the effects). The tools designed to detect potential failure are known as condition-monitoring instruments. The amount of time which elapses between the point where a potential failure is predicted by a symptom and the point where the deterioration reaches a level ISSN: 1790-5095

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Buses opacity values (m-1)

2. Published literature about conditionmonitoring inspection interval (e.g., papers, manuals, books) and historical data. To predict inspection intervals, it is recommended to use reference values as the ones inserted in table 2 referring to vehicles equipped with compression ignition engines (Diesel). It corresponds to extracts of tables from the MOBILE model with respect to model year classes between 1994 and present date. The differences are shown as changes in the “zero mile” emissions. Pollutant

After 1993 – 01 – 01:

Approved

Carbon Monoxide (CO)

Nitrogen

Oxides

(NOx) 1994-1997

Nitrogen

Oxides

(NOx) 1998-2003

Nitrogen

Oxides

(NOx) 2004 …

Particulate Mater (PM)

0,30

0,00084483

1,45

0,003379321

6,27

0,002534491

5,00

0,002534491

2,50

0,002534491

0,10

0,003379321

The Euro III limits and the values limit specified for Mercedes-Benz O 530 CITARO manual, taken as reference, are the following:

Hydrocarbons (HC) Carbon Monoxide (CO)

Nitrogen Oxides (NOx)

Particulate Mater (PM)

Smoke (opacity) ESC & ELR

Euro III limits

CITARO O 530 reference values

(g/bkW.h)

(g/bkW.h)

0,66

0,13

2,10

0,71

5,00

4,67

0,10

0,058

0,80 m-1

0,46 m-1

Without turbocharger

< = 2,5

> 2,5

Soot Density (mg / m3 ) =

109 * K Sp

Mass Fraction of Soot (mg / kg ) =

(1)

109 * K 8314,34* Temp (2) * Sp 101325* 28,9

Where Sp is the Constant for Smoke Density; if (Temp=553 Kelvin) and if (Temp623 Kelvin) than Sp=110769. More recently [8] has undertaken a study to assess some alternative measurements methods for elemental carbon. As part of this study, they compared the benchmark thermo-gravimetric mass measurement method with the commonly used real time opacimetry using laser-induced incandescence or photoacoustic sensor measurement methods.

Table 3 - BUS emissions referring to Euro III and Mercedes O 530 CITARO According to Directive number 72/306/CEE and posterior regulation, relatively to subsequent approvals dated by October 2000, for Euro III, the concentration level should not exceed the absorption coefficient limit value of 0,8 m-1. However, Portuguese legislation is using in its inspection centres, the following opacity values limit:

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> 3,0

Under the transport emissions ambit, several studies were undertaken, attempted to quantify the correlation between opacity and mass emissions. The objective of such studies was to quantify the benefits of a smoke opacity-based inspection and maintenance program and to provide data useful for selection of correlated opacity failure points. When the homologation testes are made, a key issue defines what driving cycle should be used to represent an accurate measure of vehicle emissions in-use. Several works were developed. Since [2] was possible to show that Bosch smoke number appears correlated reasonably well with total PM10, but it shows better correlation with the non-volatile (soot) fraction of PM10. Note that PM2.5 is a newer size range used by regulations and health models [5]. Bosch smoke meter draws a sample of exhaust through a filter disc which is then photoelectronically measured for its “blackness” and produces a Bosch smoke number of between 0,0 (white) to 9,9 (intense black). According [9] it is possible to calculate the soot values, when it is known the corresponding value of the absorption coefficient, k.

Table 2 - Adapted of Bus Diesel Engine Emissions Rates Used in Mobile6

Pollutant

< = 3,0

2.1 Opacity Measurement Techniques

(g/bkW.h) (HC)

With turbocharger

Table 4 - Opacity limit values (Source: Portuguese Law DL N. 554/99, December 16)

Zero km Deterioration (g/bkW.h/10000 kms) Level

Hydrocarbons

Rejected

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2.2 Tests Specifications and Conditions Under present research, the engine is installed on a vehicle and testes of escape gases opacity measurement are made under free acceleration, (engine disconnected from clutch and press the accelerator pedal since the speed of idle to the court speed). Vehicles are brought to testes after operating conditions or analyzed after a warming up period. The indicating dial of the opacimeter shall enable an absorption coefficient of 1,7 m-1 to be read with an accuracy of 0,025 m-1. During the trial, 4 Diesel Bus 2003 Mercedes CITAROS O530 has been used in 4 Groups of urban routes. The length of the routes was more or less the same. Weekly, and alternatively, 2 buses are tested in parking / maintenance centre, with evaluation of environmental parameters. Experimental procedures used for PM, gases and noise measurements, were the following: 1. To eliminate the effect of cold starts, the vehicles are warm up. Consequently, the testes are developed only in hot conditions; 2. If bus engine is not hot (70º C in refrigerator liquid), then it is placed in operation for at least thirty minutes until the engine reaches a steady running temperature; 3. The bus engine is put to work at idle, 600 rpm, and measures are collected; 4. The bus engine is put to work at 1150 rpm, and measures are collected; 5. The bus engine is put to work at 1630 rpm (maxim speed) and measures are collected. To get a preliminary picture of how fuel consumption and exhaust emissions are affected by street types and driving pattern, two types of parameters were comprehensively explored in this study. The relations seem to be complex as is reported in [6] and a modelling approach ought to be preceded by an explorative analysis. About exploitation conditions, traffic flux significantly influences the emissions and respective degradation factors. In each one of 4 lines studied, different passenger’s level boarding and alighting the vehicle, using the bus stops placed in different network zones. Therefore, each option of buses assignment per different lines is influenced by the vehicle characteristics, the sequence of arcs/nodes that serves and its service frequency. Figure 4 synthesizes the main influences over environmental indicators degradation and, consequently, over the vehicles states. The evaluation tries to demonstrate that exist correlations between the three vertices of the buses trilogy (figure 5).

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Fig. 4 - State degradation factors of urban buses

Fig. 5 - Buses trilogy Under this research, driving profile is characterized by the following classes of values, and associated acceleration and deceleration classes: Velocity class [km/h]

Acceleration class [m/s2]

Deceleration class [m/s2]

0 < v