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Estudio de fiabilidad, mantenibilidad y disponibilidad aplicado a grupos electrógenos prime

 

 

Reliability, maintainability and availability study applied to prime generator sets.

 

César Marcelo Gallegos Londo= ño.[1], Mayra Alexandra Viscaíno Cuzco.[2]= & Sergio Raúl Villacrés Parra.= [3]

 

 

Recibido: 13-04-2020 / Revisado: 17-05-2020 / Aceptado: 09-06-2020 / Publicado: 03-07-2020

 

Resumen.                         = DOI: https://doi.org/10.33262/concienciadigi= tal.v3i3.1266

El presente estudio está enfocado en proporcionar una serie lógica de pasos necesarios para el cálculo de los indicadores de fiabilidad, mantenibilidad y disponibilidad, para el ejemplo se tomaron los registros de una base de datos de 51 generadores prime, se recolectaron los tiempos operativos entre fallo= s y los tiempos de reparación de dichos fallos por un período de 11 meses. La fiabili= dad de un equipo depende de su diseño y construcción, para medir la fiabilidad es necesario registrar los tiempos operativos entre fallos, con estos tiempos calcular su tiempo me= dio, conocido por sus siglas en ingles MTBF (mean time between failure).  El análisis de los tiempos de reparació= n de estos fallos y su media MTTR (mean time to repair) es la ba= se de los estudios de la mantenibilidad. Las estrateg= ias de mantenimiento deben enfocarse para extender los tie= mpos medios entre fallos y reducir los tiempos medios de reparación, con la finalidad de conservar la fiabilidad intrínseca de los equipos. Cabe mencio= nar que el mantenimiento preventivo no puede aument= ar la fiabilidad por diseño de un dispositivo, si se r= ealiza una buena planificación del mantenimiento podemos conservarla. Con la relac= ión de estos indicadores mencionados se puede calcular la disponibilidad intrínseca. Otro aspec= to importante es poder pronosticar la fiabilidad y= la mantenibilidad, para ello es necesario tener un tratamiento estadístico con= el estudio de funciones de distribución. El análisis de la fiabilidad, mantenibilidad y disponibilidad (análisis RAM p= or sus siglas en inglés) es una herramienta fundamenta= l para predecir el rendimiento de equipos, provee información adecuada para anticiparnos a eventos de falla, esto proporcionará tiempo de reacción suficiente para tomar estrategias adecuadas. El pronóstico de la fiabilidad además es una herra= mienta muy útil cuando se requiere diseñar y construir equipos.

Palabras claves: Disponibilidad, Fiabilidad, Mantenibilid= ad, Análisis RAM, Mantenimiento.

 

Abstract.<= /b>

The present stud= y is focused on providing a logical series of necessary steps in order to calcul= ate the reliability, maintainability and availability indicators. For this exam= ple, records were taken from a database of 51 prime generators, operating times between failures and their respective repair time were collected within a period of 11 months. The reliability of an equipment depends on its design = and construction, in order to measure the reliability, it is necessary to record the operating times between failures and use them to calculate the average = time between failures, this times are used to calculate the meantime, known as M= TBF (mean time between failure). The analysis of the failures repairing times a= nd its mean MTTR (mean time to repair) is the basis of the maintainability studios. The maintenance strategies must focus in extending the mean time between failures and reduce the repairing mean times, with the purpose of keeping the equipment intrinsic reliability. It should be mentioned that preventive maintenance cannot increase a device reliability given by design= , if a good preventive maintenance planning is carried out, the design reliabili= ty can be preserved. With these two indicators it is possible to determine the intrinsic availability of the equipment. Another important aspect is to be = able to predict the reliability, availability and maintainability of the equipme= nt, therefore it is necessary to have a statistical treatment with the study of distribution functions. Reliability, availability and maintainability analy= sis (RAM analysis) is a fundamental tool to predict equipment performance, it provides adequate information to anticipate failure events, this provides enough reaction time in order to take appropriate strategies. Reliability forecasting is a very useful tool when equipment design and build is requir= ed.

=  

Keywords: Reliabilit= y, Availability, Maintainability, RAM Analysis, Mainte= nance.


Introducción.

Los grupos electrógenos Prime son máquinas que pueden generar energía eléctrica de forma continua (24 horas, 7 días), de respaldo o principales, logran trabajar al cien por ciento de su carga nominal (máxima potencia de diseño), con pausas únicamente para mantenimientos preventivos, trabajan ad= emás con carga variable, son utilizados en sitios donde la energía convencional = no existe como campos petroleros aislados, sectores rurales, etc. El rendimien= to de este tipo de equipos baja si los intervalos de las tareas de mantenimien= to preventivo no se ejecutan con las frecuencias y los procedimientos adecuado= s, además de usar los materiales especificados por el fabricante, lo que ocasi= onaría un desgaste prematuro de sus componentes. El análisis de la fiabilidad mantenibilidad y disponibilidad conocido como análisis RAM por su sigla en inglés, Reliability, Avail= ability, Maintainability, son importantes por varias raz= ones como: el análisis de la seguridad de funcionamiento de los sistemas, el cumplimiento de sus funciones y el estudio de los costos de inversión, operación y mantenimiento durante el ciclo de vida del activo (Deparment Of De= fense, 2005).

La fiabilidad es definida= como la aptitud de un elemento para realizar una función requerida en condiciones indicadas y durante un período de tiempo especific= ado (CEN, 2018). La fiabilidad puede ser analizada en el campo estadístico como= la probabilidad de que un equipo realice su funció= n prevista en un período de tiempo establecido (NACHIAS, 1995). La fiabilidad es una característica de diseño, un equipo es tan fiab= le como fue diseñado y construido, las estrategias de mantenimiento deben enfo= carse en conservar la fiabilidad intrínseca de los equipos (= MoubraY, 2002).

La Mantenibilidad es también una característica de di= seño, se expresa como la probabilidad que un elemento sea conservado o devuelto a= un estado en que pueda cumplir su función, cuando = estas actividades son realizadas de acuerdo con procedimientos y recursos establecidos (CEN, 2018) (Depatment<= /span> Of<= /span> Defense, 1966). Un equipo tiene mejor mantenibilidad cuando = es más rápido y fácil realizar las tareas de mantenimiento, la mantenibilidad es u= n campo muy amplio, en este estudio se tomará en cuenta únicamente los tiempos empleados en tareas= de mantenimiento correctivo. La disponibilidad es función de dos parámetros: de la frecuencia con = la que se producen los fallos y de la velocidad con la que se los corrige. El tiempo de inactividad no programado de un equipo depende directamente de la rapidez para repararlo y restaurarl= o y del tiempo utilizado para el soporte logístico necesario para realizar estas actividades. La definición normalizada de la di= sponibilidad se define como la capacidad de estar en un esta= do que pueda cumplir su función requerida en condiciones dadas y con los recursos necesarios (CEN, 2018) (Crespo, Moreu, & Sánchez, 2004).Los factores que inciden en la disponibilidad se representan en la Figura 1.

Figura 1. Factores que afectan a la Disponibilidad     

 

 

 

Fuente: Elaboración propia.

Muchos factores afectan el desempeño de los equipos, = entre otros: el contexto operacional donde trabajan los activos; el diseño y la calidad en su fabricación; cómo se los opera; c= ómo se los mantiene; la logística de mantenimiento, y = los métodos de detección de fallos (BS-EN, 2016).

Para el cálculo de los indicadores de fiabilidad, mantenibilidad y disponibilidad, como se mencionó anteriormente es necesario recolectar los tiempos operativos entre fallos y los tiempos de reparación de los equipos; para realizar  los cálculos citados se requiere  la integridad de estos da= tos: primero deben cumplir con las definiciones de la confiabilidad;  para su manejo debe estar previsto un procedimiento de almacenamien= to idóneo; además se debe analizar el tamaño de la población de la muestra, la cual  debe ser representativa para = un análisis estadístico (BS-EN, 2016);  por lo tanto es de suma importancia el diseño de forma= tos detallados para su recolección.

Otro punto importante antes de comenzar a calcular los indicadores de la confiabilidad es realizar una delimitación y estructuraci= ón jerárquica de los sistemas o equipos.  En esta delimitación se debe establecer donde empi= ezan y terminan los sistemas industriales, la norma ISO 14224 (recolección e intercambio de datos de confiabilidad y mantenimiento = de equipos) nos muestra ejemplos claros sobre este punto (ISO, 2016). Un grupo electrógeno puede tomarse como un sistema, o se= lo puede dividir, un ejemplo se muestra en la figu= ra N 2.

Figura 2.<= /span> Ejemplo de límites en aplicado a grupos electrógenos (BS-EN, 2016).

Fuente: ISO 14224

Es importante que los equipos tengan un código único = que nos ayude a identificarlos y nos permita hacer registros de los eventos como fallos e interven= ciones de mantenimiento (GARCIA, 2017), para ello el primer paso es desarrollar una estructura jerárquica de los niveles del inventario técnico. La norma ISO-1= 4224 presenta una estructura para la taxonomía de eq= uipos (ISO, 2016). Par fines prácticos la jerarquía de equipos se los resume en la Figura 3.

Figura 3.<= /span> Niveles jerárquicos Norma 14224.

Fuente: Norma ISO 14224

El sistema de codificación consiste en asignar un num= ero de dígitos en cada nivel jerárquico, estos pueden ser una combinación de letras y números y el resultado es un código único (GARCIA, 2017), Figura 4.=

Figura 4. Ejemplo de un sistema de codificación=

Fuente: Elaboración propia.

Para recolectar los datos necesarios utilizados en los cálculos de los indicadores de fiabilidad, mantenibilidad y disponibilidad, se deben diseñar tablas con todos los campos requeridos, la norma ISO 14224 presenta una fic= ha para el registro de la información base, ver Tabla 1.<= /span>

Tabla 1. Recolección de datos c<= span style=3D'border:none'>on fines del cálculo de indicadores RAMS.

Categoría de datos

Datos a registrar=

Descripción

Identificación

Registro de Mantenimiento (*)

Identificación de Mantenimiento único

Identificación/ubicación del equipo (*)=

ej. número de etiqueta (Véase Tabla 5)<= o:p>

Registro de falla (*)=

Correspondiente al registro de identificación de fala (no es relevante para el mantenimiento preventivo)=

Datos de Mantenimiento

Fecha del mantenimiento (*)<= /span>

Fecha cuando fue realizada la acción de mantenimiento o planificada (fecha de inicio)

Categoría de Mantenimiento (*)

Correctiva, preventiva)

Prioridad del Mantenimiento<= /span>

Prioridad alta, media o baja=

Intervalo (planeado)<= /p>

Calendario o intervalo operativo (no relevante para mantenimiento correctivo)

Actividad de Mantenimiento

Descripción de la actividad de mantenimiento

Impacto en planta de las operaciones de mantenimiento

Cero, parcial o total=

Sub-unidad mantenida

Nombre de la sub-u= nidad mantenida (puede ser omitido del mantenimiento preventivo).

Componente/ítem mantenible(s)

Especificar el componente/ítem mantenible(s). Que estuvo en mantenimiento (se puede omitir del mantenimi= ento preventivo)

Ubicación de repuestos

Disponibilidad de repuestos (p.ej. fabr= icante local/a distancia)

Recursos de Mantenimiento

Horas hombre mantenimiento, por discipl= ina

Horas hombre mantenimiento por discipli= na (mecánico, eléctrico, instrumentos, otros)

Mantenimiento horas hombre, total<= /o:p>

Total, de horas hombre de mantenimiento=

Fuente:<= /b> ISO 14224

Para el estudio del análisis RAM se deben conocer las funciones estadísticas relacionadas con los estudios del fallo y la optimización de los tiempos de reparación, entre ellas tenemos: = la función de la densidad de probabilidad f(t), la función de distribución acumulada de la probabilidad F(t), función de la fiabilidad R(t) y función de la tasa de fallos λ(t) (CRESPO, MOREU, & SANCHEZ, 2004).

Las expresiones de las distribuciones acumuladas más utilizadas para el cálculo de la fiabilidad y mantenibilidad se describen en la Tabla 3 (NACHIAS, 1995) (ACUÑA, 2003) (KNEZEVIC, 1966) (MERUANE, 2014)

Tabla 2. Distribuciones acumuladas de probabilidad.

Distribución

Función<= /b>

Dominio<= /b>

Exponencial

1-exp(t/)

t ≥ 0

Normal

ɸ[(t-) / β]

-∞ < t < +∞

Log.Normal

ɸ[ln(t-ˠ)-α) / β]

t ≥ ˠ, t ≥ 0

Weibull

1-exp-[(t-ˠ) / (α-ˠ)]β

      t ≥ ˠ, t ≥ 0

Fuente:<= /b> J. NACHIAS, Fiabilidad, Madrid: Isdefe

Una comparación del comportamiento de las funciones estadísticas en el tiempo se representa en la F= igura 5. (Red Temática Nacional Sobre Seguridad de Funcionam, 2010)

Figura 5. Comparación de las funciones estadísticas

Fuente: Red Temática Nacional Sobre Seguridad de Funcionamiento, Aplicacione= s a la   Confiabilidad,

Por su adaptabilidad la distribución de Weibull es la= más utilizada para cálculos de fiabilidad y mantenibilidad, pues posee parámetros que se adaptan a los diferentes estados de la vida útil de los equipos (Red Temática Nacional Sobre Seguridad de Funcionamiento 2010) (PARRA & CRESPO , 2016); muy utilizada en = el estudio de tiempos de vida útil o tiempos para la falla en elementos mecánicos; una característica muy importante de la distribución del Weibull es que puede tomar propiedades de otras distribuciones dependiendo de los valores que toman sus parámetros. (YANEZ, GOMEZ, & VALBUENA, 2004).

Las fórmulas de distribuc= ión acumulada para los cálculos de la Fiabilidad (R= ), la Infiabilidad (F) y La Mantenibilidad (M) en función de los paráme= tros, α (parámetro de escala) y β (parámetro de forma) son:

F(t)=3D1-e^(-[t/α ]^β ); R(t)=3De^(-[t/^= 5; ]^β )=3DM(t)=3D1-e^(-[t/α ]^β )          (1)

Metodología.

Una metodología para el desarrollo de los análisis RAM se resume en los siguientes pasos (MARTIN= EZ, 2018):

Registro de datos: tiempo entre fallos, tiempos de Operación, tiempo fuera de servicio por fallos) (CRESPO, MOREU, & SANCH= EZ, 2004) (MERUANE, 2014) (HARRIS, 2000). Para el registro de los tiempos operativos entre fallos es recomendable que los equipos cuen= ten con horómetros, estos datos deben tomarse diariamente. Para el registro de = los tiempos de reparación es necesario anotar las fechas y horas de inicio y fi= n de la reparación, las horas hombre dedicadas a mantenimiento por diciplina, la demora de los tiempos logísticos y administrativos, no se debe tomar en cuenta los tiempos que no estén relacionados con el mantenimiento, en el registro debe anotarse las tareas realizadas y qué componente fue afectado.

Comprobación de la valía de los datos (eliminación de factores extremos). Hay que tener cuidado con los datos atípicos, generalme= nte muy alejados de la media y su desviación estándar, ejemplo los tiempos utilizados para reparaciones mayores (overhaul).

Comprobar el ajuste de la distribución. Prueba realiz= ada para determinar a qué tipo de distribución se ajustan los datos, para lo cu= al es necesario realizar la linealización y se debe verificar la distribución = que tiene el mejor índice de determinación.<= /span>

Cálculo de los índices de fiabilidad y mantenibilidad utilizando la distribución más adecuada.

Determinación del parámetro β (parámetro de form= a) de la distribución de Weibull para intuir la etapa en que se encuentra la vida= del equipo (curva de la bañera)

Cálculo de la disponibilidad en función del tiempo me= dio entre fallos y del tiempo medio para reparar.

Resultados.

Luego de la recolección de los tiempos entre fallos y= los tiempos de reparación de los 51 grupos electrógenos prime seleccionados para el estudio por un período de once = meses, a los datos se les realizó la prueba de puntuación Z para determinar si existen valores atípicos, que estén muy alejados de la media y su desviación estándar, a ecuación 2

 

Z =3D (Dato evaluado – m) / Ϭ  (2)

Donde:

m=3D media

Ϭ=3D Desviación Estándar

La puntuación Z es atípica cuando su valor es menor a= -3 o mayor a 3.

Si un valor es considerado atípico es muy importante verificar los registros realizados en la recolección de datos para encontrar errores, si no existen errores los datos pueden= ser validados nuevamente para los cálculos de la confiabilidad.

Los datos válidos para continuar con él análisis se muestran en la Tabla 3

Tabla 3. Datos tiempos de operación y reparación.          =                         =                 

Tiempos entre fallos horas

2645

2512

3169

3725

3078

1783

3842

3943

3896

252

3026

1658

855

821

2282

851

330

4037

3495

2772

3326

3173

1220

1819

76

3800

4467

2025

723

3915

4315

1777

3596

1206

3442

990

183

2153

4114

919

838

4181

2648

1523

4463

3267

588

3340

704

4070

706

2441

1187

1468

2705

2163

2866

3892

3779

757

1624

3755

800

2996

2461

2554

1804

4137

3304

1370

691

1135

3372

1334

3982

614

713

4008

3452

3402

190

3198

Tiempo de reparación en horas

0.27

0.32

0.32

0.50

0.50

0.50

0.50

0.50

0.75

1.00

1.00

1.00

1.00

1.00

1.00

1.00

1.42

1.50

1.67

2.00

2.02

2.50

2.75

3.00

3.00

3.00

3.32

4.00

4.50

5.00

6.00

6.00

6.32

7.00

7.40

7.60

7.87

8.00

8.12

8.20

8.25

8.30

8.48

8.50

8.82

9.00

9.00

9.15

9.62

9.92

10.30

10.52

10.62

11.22

13.00

14.05

14.77

14.97

15.42

15.75

15.92

16.32

16.45

16.57

16.82

17.22

17.30

19.52

19.90

21.02

21.37

21.42

21.47

22.20

22.25

22.42

22.50

22.62

22.70

23.22

24.00

47.50

 

 

 

 

 

 

 

 

Fuente:<= /b> Elaboración propia.

El siguiente paso fue realizar las linealizaciones pa= ra las distribuciones, exponencial y Weibull, que son las más utilizadas en el contexto de la confiabilidad, c= on esto se verificó a qué tipo de distribución se ajustan los datos. El criter= io utilizado para esta evaluación es el índice de determinación el cual indica que porcentaje de los puntos (coordenadas) están relacionados linealmente, identificado como R2.<= /span>

Linealización Distribución De Weibull.<= /b>

La Ecuación bi-paramétrica para el cálculo de la fiabilidad es:

R(t) =3D e-(t/α)β    (3)

Aplicando los logaritmos neperianos a la función R(t) por dos ocasiones obtenemos:

ln<= /span> ln<= /span> {1/[1-R(t)]}=3D β ln (t) – β lnα    (4)=

La expresión anterior se la compara con la ecuación d= e una recta.

Y =3D Bx + C      (5)=

 Donde:               =                    Y =3D ln ln<= /span> {1/[1-R(t)]}     (6)

Bx<= /span> =3D β ln(t)     (7)

C =3D -β lnα    (8)=

Para obtener las coordena= das para la linealización, se aplica:

Para el eje (Y):    = ln [ln= (1/1-Rango Mediana)]=    

Para el eje (X):    =    ln(t)    (10) 

Una manera de realizar aproximaciones es por medio del rango de las medianas, que ayuda al cálculo de = las probabilidades acumuladas, para ello utilizamos= los estimadores matemáticos como las fórmulas de Bernard, estas fórmulas depend= en del tamaño de la muestra a analizar:

N>50              =              M=3D i/M    (11)

50> N >20      =             M=3D i/N+1   (12)

N<20              =             M=3D i-0.3/N+0.4   (13)

Donde:

i=3D Numero de orden de l= a muestra

N=3D Tamaño de la muestra

M=3D Rango de las medianas

Los resultados de la linealización para la distribuci= ón de Weibull son:

Recta de linealización                       Y =3D 1.4176X-11.17

Parámetro de forma                           B=3D β=3D= 1.4117

Intercepto           =                                 C=3D -11.17

Índice de determinación                     R2=3D 0.93

Linealización Distribución De Exponencial

La función de la fiabilidad para la distribución exponencial es:

R(t)=3D e(-λt)           (= 14)

Donde:

λ=3DTasa de fallos =3D 1/MTBT

t=3D Tiempo hasta el fallo

Las coordenadas para la linealización exponencial son= :

Y=3D Ln[1-F(t)]

X=3D tiempo operativo hasta el fallo

Donde:

F(t)=3D Función de densidad de probabilidad

Ln<= /span>=3D Logaritmo natural

MTBF=3D Tiempo medio entre fallos

Los resultados para la linealización de la distribución exponencial son:<= span lang=3DES-EC style=3D'font-size:12.0pt;line-height:115%;font-family:"Times = New Roman",serif; mso-fareast-font-family:"Times New Roman";border:none;mso-ansi-language:ES-= EC'>

Tasa de fallos       =                               λ=3D0.000532926

Tiempo medio entre fallos                 MTBF=3D 1876.431

Recta de linealización                                    Y=3D-0.000532926X+0.3493867

Índice de determinación                     R2=3D 0.79891

Con estos datos se obtuvieron las rectas de linealiza= ción para las distribuciones analizadas:

Figura 6.<= /span> Comparación Índices de Determinación

Fuente: Elaboración propia.

Para seleccionar el modelo de distribución más adecua= do se calcularon los coeficientes de determinación. La distribución seleccionada = es la de Weibull, su índice de determinación es superior, R2=3D0.93.

Con la ecuación de la recta de linealización de la di= stribución de Weibull Figura 6, se tiene la pendiente = de la recta, que es el parámetro de forma β de la función de distribución, y= con el punto de intersección de la recta con el eje= y (el punto C), podemos obtener el parámetro de escala α con la expresión:

α=3De(-C/β)    (15)

 α=3D 28= 45,77684

Con los parámetros α y β de la distribución= de Weibull se calcula la función de la Fiabilidad R(t) reemplazando varios val= ores del tiempo (t) Figura 7.

Las ecuaciones para el cálculo de la Fiabilidad e Infiabilidad para la distribución de = Weibull son:

Fiabilidad           =                    R(= t)=3D e-[t/α]β   (16)

Infiabilid= ad                            F(t)=3D1 -  e-[t/α]β         (17)

 

Figura 7. Curva de la fiabilidad.

 

 

Fuente: Elaboración propia.

Para calcular el Tiempo medio entre fallas MTBF con <= span style=3D'border:none'>los parámetros de la dist= ribución de Weibull de aplica la distribución Gamma.

                                                =                  MTBF =3D α x Γ (1+1 /β) (18)

MTBF=3D 2405

Se repite el mismo procedimiento para el cálculo de la mantenibilidad, se debe utilizar los datos de los tiempos de reparación. La expresión utilizada p= ara Mantenibilidad es:

F(t)=3D1 -  e-[t/α]β  (19)

El tiempo medio para reparar en función de los paráme= tros de Weibull se obtienen con la expresión:

MTTR =3D   &#= 945; x Γ (1+1/β) (20)

MTTR=3D 9.99

La Disponibilidad se calc= ula en función del MTBF Tiempo Medio entre Fallos y el MTTR Tiempo Medio para Repa= rar.

La ecuación para la disponibilidad intrínseca (por di= seño) es:

Disponibilidad =3D MTBF/ (MTBF+MTTR)    (21)

Disponibilidad =3D 2405/ (2405+10) X 100= =3D 99.5%

Un resumen de los cálculos probabilísticos de la fiabilidad, infiabilidad se presentan en las Figura 8.

Figura 8.<= /span> Curva de la Fiabilidad e Infiabilidad

 

Fuente: Elaboración propia.

Los cálculos de la mantenibilidad en forma son iguales a los de la Infiabilidad, con la diferencia que se trabajan con los tiempos de reparación.

Un resumen de los cálculos de la mantenibilidad se pr= esenta en la figura 9.

Figura 9. = Curva de la Mantenibilidad.

Fuente: Elaboración propia.

Discusión.

El resultado obtenido al calcular el índice de determinación de la distribución de Weibull es mayor al ín= dice de determinación obtenido para la distribución exponencial lo que garantiza= un mejor ajuste de los datos para la distribución de Weibull para el cálculo de los indicadores RAM.=

Con los parámetros de la distribución de Weibull se calcula la probabilidad que el equipo funcione correctamente para cualquier tiempo de ensayo, ejemplo a las 1,000 horas la probabilidad que un equipo funcione correctamen= te es del 77.77%, y la probabilidad de fallo será por= lo tanto de 22.23%, ver Figura 8. Con esto es posible estimar la probabilidad = que un equipo falle en un período de tiempo, el cual será seleccionado de acuer= do a las necesidades del estudio a realizar.<= /span>

Para el cálculo de la mantenibilidad se operó con la misma metodología, ejemplo la probabili= dad que un equipo esté reparado a las 10 horas es del 64%, a las 20 horas es 86% ver Figura 9. En el caso del estudio de la mantenibilidad se puede pronosti= car la probabilidad de que un equipo esté reparado en un cualquier lapso de tiempo de interés, indic= ador muy válido para evaluar estrategias que permitan disminuir los tiempos de reparación.

El parámetro de forma β de la distribución de We= ibull para el cálculo de la fiabilidad, índica en que= parte de la vida se encuentran los elementos (curva de la bañera), en el estudio realizado el valor de β es 1.39 lo que indica que los equipos no han entrado la etapa de envejecimiento que se caracteriza por el aumento de la tasa de fallos. En la práctica el parámetro = 46; debe superar el valor de 3 para considerar que los equipos se encuentran en= la etapa de envejecimiento.

El tiempo medio entre fallos obtenido es 2405 horas (= cada 2405 horas se espera que uno de los 51 equipos analizados falle) y el tiempo medio para la reparación es de 10 horas. Los tiempos de operación son muy superiores a los tiempos de reparación para el estudio realizado, la disponibilidad total calculada es del 99,5%. Esta información confirma que la mayoría los equipos= no han entrado en la zona de desgaste (el tiempo medio entre fallas es alto y = los tiempos de reparación son bajos).

Conclusiones:

·         El paso primordial para el cálculo de los indicadores es la validez de los datos, es necesario implementar un método con formatos definidos para la recolección= de la información de los indicadores RAM (tiempo operativo entre fallos y los tie= mpos necesarios para la reparación).

·         El tiempo de reparación de un equipo está dividido en: tiempo para la= detección de la falla, tiempo logístico para conseguir y organizar los recursos necesarios, tiempo activo= en la reparación, tiempos de pruebas e incluso tiempos de traslado. Para el análisis de la mantenibilidad es necesario incluirlos todos.<= /span>

·         Los estudios de la fiabilidad pueden enfocarse tomando varios criterios, por eje= mplo, se pueden calcular para flotas o grupos de equipos o para cada equipo, tamb= ién se los puede analizar para un determinado modo de fallo o para todos los mo= dos de falla ocurridos, esta variedad de posibilidades proporcionará información diferente, lo c= ual es un apoyo importante para la toma de estrategias para mejorar la fiabilidad = de los equipos.

·         Los indicadores de fiabilidad y mantenibilidad proporcionan informaci= ón general válida, determinan hacia donde debemos dirigir los  esfuerzos para la conservación adecuada de las funciones de los equi= pos, dichas acciones deben estar encaminadas alargar los tiempos entre fallos y disminuir los tiempos de reparación, para afirmar estas estrategias se deben apoyar en otras metodologías proactivas como el Análisis Causa Raíz, Análisis de los Modos = de Fallos y Efectos, Mantenimiento Centrado en Fiabilidad, Análisis de Pareto, etc.

Referencias bibliográficas:

Acuña, J. (2003). Ingeniería de la Confiabilidad. Costa Rica: Tecnológica de Costa Rica.

BS-EN. (2016). ISO 14224 Recolección e intercambio de datos de confiabilidad y mantenimiento de equipos. BSI Standards Limited.

CEN. (Julio de 2018). Terminología de mantenimiento. EN-13306. ARENOR.

Crespo, A., Moreu, P., & Sánchez, A. (2004). Ingeni= ería de Mantenimiento. Madrid: AENOR.

Deparment Of Defense, = U. (3 de Agosto de 2005). Guía para lograr la Fiabilidad, Disponibilidad y Mantenibilidad. USA: Deparment of Defense .

Depatment Of Defense, U. (24 de Mayo de 1966). MIL= I-HDBK-472. Manintainability Prediccti= on. Washington, USA: DEPATMENT OF DEFENSE, USA.

GARCIA, O. (2017). Gestión Moderna del Mantenimiento Industrial; Principios fundamentales. Ediciones de la U.

Harris, M. (2000). Gestión del Mantenimiento Industrial. Madrid: Fundación REPSOL.<= o:p>

Knezevic, J. (1966). Mantenimiento. (4ta, Ed.) Ma= drid: Isdefe.

Martinez, E. M. (2018). Seminario de Confiabilida= d.

Meruane, V. (2014). Gestión de Activos. Santiago: DEMEC.

Moubray, J. (2002). Mantenimiento Centrado en la confiabilidad. Asheville: Industrias press Inc.=

Nachias, J. (1995). Fiabilidad. Madrid: Isdefe.

Parra, C., & Crespo , A. (2016). Métodos de análisis de fiabilidad, mantenibilidad, disponibilidad y riego. Ingecom= .

Red Temática Nacional Sobre Seguridad de Funcionam.= (2010). Aplicaciones a la Confiabilidad, Aplicaciones Prácticas. Madrid: Ingeman.

Yanez, M., Gomez, = H., & Valbuena, G. (2004). Ingeniería de la confiabilidad y análisis probabilístico del riesgo. Reliability and risk managemeny S.A.=

 

 

 

 

3Dedi=

 

 

 

 

 

 

 

PARA CITAR EL ARTÍCULO INDEXADO.

 

 

 

Gallegos Lond= oño, C. M., Viscaíno Cuzco, M. A., & Sergio Raúl= , S. R. (2020). Estudio de fiabilidad, mantenibilidad y disponibilidad aplicado a grupos electrógenos prime . Conciencia= Digital3(3), 44-61. https://doi.o= rg/10.33262/concienciadigital.v3i3.1266

 

 

3Dedi=

 

 

 

 

El artículo que se publica es de exclusiva responsabilidad de los autor= es y no necesariamente reflejan el pensamiento de la Revista Ciencia Digital.

 

 

El artículo queda en propiedad de= la revista y, por tanto, su publicación parcial y/o total en otro medio tiene = que ser autorizado por el director de la Revista Ciencia Digital.

 

 

3D"logo_catalogo3b.jpg"
<= /o:p>

 

 

 =



[1] Escuela Superior Politécnica de Chimborazo, Facultad de Mecánica, Riobamba, Ecuador.

   cesar.gallegos@espoch.edu.ec

[2] Escuela Superior Politécnica de Chimborazo, Facultad de Mecánica, Riobamba, Ecuador.

   mayra.viscaino@espoch.edu.ec

[3] Escuela Superior Politécnica de Chimborazo, Facultad de Mecánica, Riobamba, Ecuador.

   sergio.villacres@espoch.edu.ec<= /span>

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                                                                        =                                          ISSN: 2600-585= 9

=                         Vol. 3, N°3, p. 44-61, julio-septiembre, 2020

<= o:p> 

Educación <= span lang=3DES-EC style=3D'font-family:"Times New Roman",serif;mso-bidi-theme-fo= nt:minor-bidi; color:windowtext;mso-ansi-language:ES-EC;text-decoration:none;text-underlin= e: none'>del Futuro                                                                               =                                               Página 54

 

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