Condition monitoring can substantially reduce the cost of consequence by avoiding catastrophic failures and reducing the frequency of incidents by providing realtime and continuous diagnoses of bad actors and all relevant components.

Perspective 1: Operational Benefits

In terms of operational benefits, a CM system will positively affect: equipment uptime, mean time between maintenance (MTBM), component lifetime, production rates and overall operations effectiveness (OOE) of the process and plant. In particular, a CM system allows the machine to operate until scheduled shutdowns, although some values (e.g., vibrations) are less than perfect. Performance diagnostics enable process managers to save energy costs and increase asset efficiency. Early detection of leaking valves, seals, or piping saves penalties for environmental pollution.

Remote access from the system vendor allows expert support based on the customer’s systems data and avoids expensive traveling to the site(s) where the system is installed, an important element for offshore applications, such as floating production storage and offloading (FPSO) units, LNG carriers and others.

Perspective 2: Maintenance Benefits

A CM system reduces maintenance campaign costs with more targeted activities and fewer work orders. It can replace the need for preventive, offline measurements, which are often executed by costly external service companies, and reduce labor time, associated costs and mean time to repair (MTTR). Moreover, the knowledge of a failed or failing component allows targeted repairs instead of trial-and-error campaigns, with less capital commitment for spare-part inventory.

Perspective 3: Risk Avoidance

Risk is the product of two factors: consequence and probability (frequency). In the following analysis, the factor consequence is presented in monetary terms. Let’s say, for example, that a compressor fails catastrophically, with a potential consequence of US$200,000 in production loss, labor costs and spare parts. If this event occurs at a frequency of once every ten years, this represents an annual risk of US$20,000 per year, but if it occurs every two years, the annual risk is US$100,000.

Experience shows that CM pays back significantly fast, especially during the initial start-up of new machinery or after major overhauls or main process changes.

A Worthwhile Investment?

Condition monitoring can substantially reduce the cost of consequence by avoiding catastrophic failures and reducing the frequency of incidents by providing realtime and continuous diagnoses of bad actors and all relevant components. The risk of machine failures has several severity steps that need to be considered when starting the ROI calculations: normal loss (cost for production loss and maintenance campaigns during scheduled shutdowns), probable maximum loss (cost for massive maintenance or new machine with associated production losses) and  maximum possible loss (massive machine damage, loss of product, health, safety and environment (HSE) issues, environmental pollution, fire, business interruption). When it comes to the financial justification of investments in CM, many studies assume the system is perfect and will always inform the user ahead of any impending failure. However, this is not always the case, and false alarms and missed failures will produce costs. These imperfections and their effects on operation and maintenance must be part of the equations as the payback periods increase.

This is what condition-based maintenance is all about: taking action only when required.

When investing in a predictive maintenance system, two methods of assessing the economic incentives can be used.

The first is the Payback Period, which gives information as to whether the system pays for itself within a defined period of time. The result is expressed in time (years, months).

Chart Payback
Chart ROI

Second, the Return On Investment measures the amount of return on an investment in a specified time period relative to money spent. To calculate the ROI, the return of an investment within a time frame is divided by the cost of the investment, and the result is expressed as a percentage or ratio.In both cases, operators have to precisely sum all costs and efforts associated with each detected failure to perform the equations. Adding up the investment is easy. More challenging, but equally important, is the realistic calculation of the benefits earned from the CM system. Four categories of benefits should be considered: One is lost production, which is perhaps the most difficult cost to determine. However, on average, reduced downtime is responsible for 60 to 70% of a company’s savings in this regard. These savings will depend on the type of machine in question. Consider, for example, a machine that produces US$10,000 worth of product per hour. By preventing a bearing failure on this machine, you could eliminate 5 hours of downtime and a US$50,000 loss in production. Second is labor. These savings are easily calculated by checking the particular machine’s repair records in the previous year. The number of hours spent on planned and unscheduled repairs gives a realistic indication of how much time a company can save after implementing the CM system.

Condition Monitoring System Scope

Calculations aside, the most important question to answer is “What system best fits our needs?” Condition monitoring systems range from handheld devices to online diagnostic systems with neural network features. The ongoing development of new technical features, system capabilities and reliability of diagnostic results has made costs for a system increase. However, the number of different production assets that benefit from continuous online monitoring has increased as well. Debottlenecking campaigns and high product-output plans require more machines to be efficient; formerly redundant machines are now onstream and an essential part of the production process. With less backup machinery available, plant operators are more dependent than ever on reliable machinery to meet production goals. In short, condition monitoring systems and programs are mandatory in modern industries.

Machine Criticality

Machine criticality is one point to start with to identify the proper monitoring technology and scope. One factor of the criticality definition is the well-known risk matrix. Again, we see the previously mentioned factors, consequence and probability. Risk assessment is a challenging and complex task; still, in terms of criticality we also have to consider aspects such as process layout (single line or multiline); list profits (per hour) in case of production loss; availability of product reserves to keep processes running downstream of failed machine; time and cost for shutdown and start-up caused by machine failure; equipment redundancy (backup machinery); average MTTR of the evaluated assets; failure history of machinery; and availability of maintenance experts and tools. The more critical the asset, the more advanced the CM technology should be.

As the probability and the severity of consequences of an incident increase, risk and the need for condition monitoring increase.

Areas which might need refining include:

  • The training of system users.
  • The proper adjustment of all warning thresholds.
  • The full utilization of all system features and capabilities.
  • Determining whether diagnostic outputs are delivered to the correct destinations so they are not ignored.

Finally, operators must build confidence in the notifications and diagnostic results issued by the system. When the system detects an uncritical-yet-unusual, wear development, operators should not immediately stop production and open the machine. Instead, they should have an eye on the trend data and keep the machine running as long as possible, that is, until the next scheduled shutdown. However, it has been shown that brand new parts and components fail in the first hours of operation. In this case, an immediate stop might be necessary to avoid consequential damages. This is what condition-based maintenance is all about: taking action only when required.

Calculating Total Cost Of Ownership

Continuous monitoring requires a larger investment for online data acquisition and analysis equipment plus installation. To precisely assess the total cost of ownership, operators must consider the following investments: system engineering and installation, field instrumentation (sensors, cabling), monitoring and diagnostic system (hardware and software, installation, software licenses), user training and customer support (if required); system maintenance (sensor replacement, software updates), necessary external expertise and support.

Another factor is Spare Parts

The machine’s maintenance records are a good way to determine the cost of replacement parts such as valves, bearings, and gears.
Last is drive power consumption. This factor is a little harder to evaluate because it is not typically included in maintenance records. But improving machine efficiency can substantially reduce drive energy cost. The rate at which companies recover an investment in CM depends on factors such as the type of products manufactured, the amount of experienced downtime, and how well they implement and use the system. In some cases, a company can recover its investment in monitoring equipment and training within months after initial start-up. Within a year, they can obtain as much as a four to five times ROI. Some full-scale CM systems can pay for themselves if they can help prevent only one  high-damage failure on a reciprocating machine.
Experience shows that a CM ROI is reached especially quickly if the system is implemented during the initial start-up of new machinery or after major overhauls or process changes. In other cases, there may be little or no return during the first few months; in fact, maintenance costs may increase during these early months because many new, unknown issues are identified, diagnosed and corrected in a short time period. Once these initial problems are corrected, however, maintenance costs drop dramatically and remain low. If the system is not providing a return after several months, a re-evaluation of its implementation might be necessary, and some adjustments may be needed.