Why monitoring?

41 Îź

C 0

38

C 1

C 1 C 0 C 0 C 0 C 1

C 3

C 4 C 2

C 3

36

C 2 C 2 C 4 C 4 C 4 C 4 C 2 C 2 C 2 C 4 C 2 C 4

C 2 C 4

C 2 C 4 C 4 C 2 C 2

C 4 C 3

33 3150 2925 2700 2475 2250

thres w

2025 1800 1575 1350 1125 900 675 450

First threshold violation on Sep 20 at 18:14 hrs

225 0

1 9 .0 . 0 7 .2 .2 0 0 6

0 1 .0 8 .2 0 0 6

1 7 .0 8 .2 0 0 6

0 2 .0 9 .2 0 0 6

Why monitoring ?

1 8 .0 9 .2 0 0 6

Why monitoring ?

Increase machinery uptime despite constant maintenance investments through optimised component lifetime utilisation for extended Meantime Between Maintenance This figure is based on the assumption that most items or components operate reliably for a period “X”, and then wear out. Classical thinking suggests that extensive records about failure will enable us to determine this “lifetime” and so make plans to take preventive action shortly before the component is due to fail in future. This model is true for certain types of simple equipment (e.g. rider rings) and for some more complex items with dominant failure modes.

In particular, wear out characteristics are often found where equipment comes into direct contact with the product. Age-related failures are also often associated with fatigue, corrosion, abrasion and evaporation. The

2% A

4% B

5% C

7% D

14 % E

68 % F

} }

11% Probability of failure connected to operating age

89% Probability of failure not connected to operating age

Pattern description: Pattern “A” shows the classic “bathtub curve” (see Pattern “B”) without the phase of “infant mortality”. Pattern “C” describes slowly increasing conditional probability of failure with no identifiable wear-out age. “D” shows low paobability of failure when the item is new than a rapid increase to a constant level, while “E” shows a constant probability of failure at all ages! Pattern “F” starts with an infant mortality behavior which drops eventually to a level of constant random failure probability.

Probability of Failure

Infant mortality

Lifetime

Wear-out zone Age

Traditional view of failure: The “bath tub curve“ displays a high failure rate after machine start (infant mortality); increase of failure rate towards end of component lifetime due to wear Source: RCM II by John Moubray, Industrial Press Inc, 1992

period of “infant mortality” at the very start of the items lifetime is mostly caused by human errors during initial start-up routines or item installation. Quite apart from greater expectations, new research is changing many of the most basic beliefs about age and failure.

In particular, it is apparent that there is less and less connection between the operating age of most assets and how likely they are to fail.

The so called “3rd generation” of failure examination revealed, that not one or two but six failure patterns actually occur in practice. One of the most important conclusions to emerge from this research is a growing realisation that although they may be done exactly as planned, a great traditionally-derived maintenance tasks achieve nothing; while some are actively counterproductive. This is especially true of many tasks done in the name of preventive maintenance. The figure shows that majority of components do not fail due to age. This finding is essential for those who are responsible for maintenance and/or operation of complex machinery and another strong reason for machinery monitoring.

Examples for the different failure patterns (left): Pattern “A”: Piston rider rings Pattern “C”: Valves Pattern “D”: Piston rod connection Pattern “E”: Packings Pattern “F”: Bearings

As described before, timing is a crucial issue in preventive maintenance planning. Given that the campaign intervals are too short - means, that the operator does not fully utilize the components lifecycle - a second downside is obvious: the frequency of the “infant mortality” period is un-necessarily high.

As a result it can be stated, that:

MTBM - too short If the applied mean time between maintenance is too short,

- component running time is dissipated

- Lifetime of components is dissipated

- machinery has more downtime than mandatory

- Infant mortality will be repeated (3/4 of all patterns show this phase)

- the risk of infant mortality has to be taken frequently increasing the overall average failure rate The todays challenge is, to increase machinery uptime despite constant maintenance investments. The solution is a optimised component lifetime utilisation for extended Meantime Between Maintenance. It can be stated, that the maximum MTBM depends on the “weakest link in the chain”, i.e. valves. So, the task is, to determine the “weakest link” (= shortest MTBM) to predict necessary overhaul stops.

This determination can the realized with state-of-theart online monitoring. With todays monitoring technology, it is possible to detect the slightest change in be-

havior of components and to receive knowledge about impending failures at a very early stage. With this knowledge available, maintenance planning becomes a totally different foundation than ten years ago.

Even through improvements in spare part materials and maintenance technology have been made throughout the years, many companies operating large reciprocating compressors started to use condition monitoring e.g. PROGNOST systems to improve machinery reliability.

Detection

About PROGNOST Systems

Some special defects might be related to process or mechanical problems. The condition monitoring is the tool to distinguish. This helps to avoid endless meetings between process and mechanical staff. Condition monitoring delivers a result that is acceptable for all involved teams.

Internationally, PROGNOST Systems GmbH is the No.1 partner for companies who want to ensure safe, reliable, and economic operation of their reciprocating piston compressors.

Spare compressor Before getting a spare compressor into service, condition monitoring can detect mechanical problems. Everything is checked before the compressor goes online.

Once the spare compressor is in service, it will be very important for production. Mechanical damages causing consecutive failures it will cause serious costs.

Your advantages + Maximum utilization of component lifecycle + Increased machinery uptime + Expert machinery condition knowledge for efficient maintenance planning + Reliable and safe machinery operation

PROGNOST Systems offers Asset Performance Systems and services based on over 15 years of engineering experience in recording, analyzing and interpreting high-frequency status data for reciprocating piston machines.

PROGNOST Systems offers the only system in the industry that records and analyzes status data in real time and compares them using “pattern recognition” based on actual experience. It provides machine operators with a timely analysis of the causes in the form of plain text information. This means that any reciprocating piston machine monitored by this system can be shut down fast an reliably with virtually no risk of false trips.

With more than 400 current installations operating for over 80 renowned corporations around the world, PROGNOST Systems is the most successful supplier of online diagnostic systems for successful predictive maintenance strategies of reciprocating compressors.

Birkenallee 177

1020 Bay Area Blvd. Suite 118

48432 Rheine

Houston, TX, 77058

Germany

USA

Phone: +49 (0)59 71 - 8 08 19 0

Phone: +1 - 281 - 480 - 93 00

Fax: +49 (0)59 71 - 8 08 19 42

Fax: +1 - 281 - 480 - 93 02

Email: info@prognost.com

Toll Free: +1 - 800 - 848 - 6677 Email: infousa@prognost.com

www.prognost.com

05/2009

PROGNOST Systems, Inc.

TI_Flyer Why monitoring A4_01_EN

PROGNOST Systems GmbH