Reliability Engineering: Preventing Asset Losses

August 19, 2020

Reliability engineering in industrial maintenance is a relatively recent function and incorporates important technological advances to contribute to the competitiveness of the industry where it is applied, adding value to the business.

In its broad role, it has many important functions:

  • Improve the availability and production capacity of critical equipment;
  • Obtain and record improvements in maintenance activities;
  • Establish proactive maintenance plans that minimize corrective maintenance usage and costs;
  • Maximize benefits from preventive and predictive maintenance;
  • Increase the life cycle of the assets.


Originated in the 1960s in the aerospace and military sectors, reliability engineering has found fertile ground in Brazilian industrial maintenance.

In this market, the petrochemical, energy, mining, paper, and cellulose sectors are becoming references in establishing data analysis models to obtain the best return from the industrial parks installed.

The Industrial Internet of Things – IIoT – will rapidly add new technologies and methods to increase the effectiveness of reliability engineering.


Reliability: the ability of an item, or a system (of assets), to perform a required function under specified conditions over a given time interval.

It is usually expressed by the MTBF indicator, that is, the mean time between failures.

Availability: is a function of reliability and maintainability. The ability of an item to be able to perform a certain function, during a given time interval.

Maintainability: is a measure of how easily and quickly a system or piece of equipment can be restored to an operational state after a failure.

Greater maintainability implies reduced repair times, which is why it is usually expressed by the MTTR indicator, that is, the mean time to repair.


Risk management is an important part of the asset management process.

Its purpose is to understand the cause, probability, and consequences of adverse events and to make the risks associated with these events acceptable, or not.

In fact, the criticality of an asset is defined by its importance (value) and vulnerability to the organization, in case of failure or non-performance of its expected function.

The reference in risk management is the ABNT ISO 31000 – 2009 Standard Risk management – Principles and guidelines.


Maintenance expenditure on risk management, i.e. monitoring the condition of machinery and process control, etc., must be directly related to the probability of failure occurring and the severity of the consequences of these failures.

However, condition monitoring is a process of systematically collecting and evaluating data to identify changes in the performance or condition of an asset system, or its components, so that proactive remedial actions can be planned in a cost-effective manner to maintain reliability.

If data availability can be a challenge in maintenance management and its risks, for ABRAMAN, from a six-month history of reliable information it is already possible to make the calculations for sufficient application of reliability engineering.


However, there are several predictive techniques that, when incorporated into a proactive maintenance strategy, will significantly contribute to the reliability, availability, and maintainability of industrial machinery.

Among the predictive techiniques are:

Vibration analysis: One of the oldest prediction methods in industry that allows the detection of potential failures such as unbalance, misalignment, shaft warping, wear on gears and bearings, poor machine or internal component attachment, abrasion, backlash, bearing wear, electrical problems, among others.

Oil analysis: Used to detect wear on moving parts in machinery and the presence of contaminating substances.

There are four types of oil analysis: physical-chemical analysis; contamination analysis; spectrometry; and ferrography.

Ferrography: The quantification and analysis of the morphology of wear particles (swarf) found in lubricant samples determines, among other things, the types of wear, contaminants, and lubricant performance.

Thermography: A non-destructive technique for measuring temperature and observing heat distribution from infrared radiation.

It seeks to identify failures in electrical equipment and systems, static and dynamic mechanical equipment.

Ultrasound: A method by which internal discontinuities are detected by the mode of propagation of sound waves through a component of machinery.

– Statistical analysis of daily temperature and acceleration time series: The use of dataloggers with temperature and acceleration sensors allows us to know the behavioral signature of a machine, identifying its tendency towards health or failure.

While spectral analysis generates a high-resolution snapshot, statistical time-series analysis generates a low-resolution movie, which, because of its richness of data, makes it possible to create a true machine medical record.

The Dynamox Solution has the ability to identify the failure trend in monitored machines and components, as well as perform spectral analysis, with modern wireless technology.

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