Maintenance philosophies can be classified into two categories. These are reactive and proactive maintenance. As the name indicates, reactive maintenance is unplanned; it simply takes corrective actions as equipment exhibits fault symptoms that humans can sense or break down.
Proactive maintenance can also be broken down into two categories:
1. Preventive maintenance (PM)
2. Predictive maintenance (PdM)
Both are planned operations to help reduce the operation and support costs and increase equipment life.
According to the data of Statista in the manufacturing sector:
“As of 2020, 76% of the respondents reported following a proactive maintenance strategy, while 56% used reactive maintenance (run-to-failure).”
According to the Market Research Future:
“The global predictive maintenance market is expected to expand at 25.5% CAGR to reach USD 23 billion in 2025 during the forecast period.”
History of Maintenance Philosophies
From the historical perspective of maintenance, one can say that the most spectacular changes have occurred in the last sixty years following World War. Until then, corrective maintenance was the only option for a maintainer where equipment used to be fixed or replaced on a breakdown basis. Nevertheless, corrective maintenance is still in use for simple components such as light bulbs or a basic pipeline which are less risky and where the failure consequences are not fatal.
From the 1950s, mechanization and automation steps have risen due to the increasing intolerance of downtime and the significantly increasing labor cost. Improved machinery was of lighter construction and ran at higher speeds, provoking wear out more quickly, leading to proactive maintenance development.
Preventive maintenance is a sub-discipline of proactive maintenance in which the maintenance tasks are performed periodically. Periods are fixed intervals determined using historical data (e.g., MTBF: Mean Time Between Failures) and without input from the actual equipment being used. Equipment is serviced on a routine schedule, whether the service is needed or not. However, both reactive and blindly proactive (preventative maintenance) maintenance approaches have financial and safety implications associated with them. Routine inspection rounds and lubrication, bi-monthly bearing replacements, or maintenance inspections and overhauls on aircraft systems are some of the examples of preventative maintenance activities.
In the late 1970s, the effectiveness of conducting preventative maintenance started to be questioned. A common concern about ‘over-maintaining’ arose, which led to the development of predictive maintenance. Adaptively determined scheduling of maintenance actions is the main feature of predictive maintenance that distinguishes it from preventive maintenance. On the contrary, predictive maintenance is limited to those applications where the cost and consequences are critical and technically feasible.
From the 1980s, systems became progressively more complex in nature, bringing a more competitive marketplace and intolerance of increased downtimes. For instance, the daily loss of revenue due to downtime is £320,000 for a Boeing 747 aircraft. Increasingly, risk analysis and environmental safety issues have become paramount, and new concepts such as condition monitoring and expert systems have emerged. The Institute of Asset Management was established in the UK in the mid-’90s, which has received significant attention from most organizations.
Since the 2000s, terms such as prognostics and industrial internet (Industry 4.0) have emerged. Today’s sophisticated sensor technology enables us to track degradation processes and empower the prognostic reasoning of monitored equipment. With the help of advancements in computing power and sensing technology and the adaptation of artificial intelligence, the processes mentioned above seem to be automated more and more.
Predictive Maintenance
As per a study, 82% of companies have faced unplanned downtime, and the lost revenue cost runs into millions of dollars. McKinsey and Company’s analysis concludes that AI-based predictive maintenance gives rise to a 10% reduction in annual maintenance costs, a 25% reduction in downtime, along a 25% reduction in inspection costs.
Predictive maintenance practices have significant advantages in reducing the support and operating costs and leading to more effective planning and operational decision-making. An unexpected one-day stoppage in the machinery industry may cost up to $225,000.
Predictive maintenance helps equipment operators perform maintenance tasks when the need arises. The necessity concept is determined by assessing the health condition of the equipment continuously.
The process starts with acquiring data and transmitting it to the higher level where the signal is processed (e.g., feature extraction and selection), followed by the practice called diagnostics to detect and isolate faults (e.g., FMECA failure mode analysis). Based on the determined health condition of the system, a prognosis can then be made by employing time series trending algorithms which help estimate the remaining useful life of the asset.
The potential-functional failure curve (P-F Curve) is a known way of illustrating the health condition of a system. The system’s performance is represented with this curve, as seen below. As expected, it declines over time, leading to a failure in the end. The aim is to detect the fault before it reaches critical levels and allow maintenance personnel to prepare and supply parts for maintenance.
Performing maintenance preparation when the system is up and running greatly affects reducing the operation and support costs. In addition to the reduced downtime, the inventory cost will be reduced as more time will be available for obtaining the necessary parts. Moreover, the logistics & supply chain efficiency will be increased through better preparation for maintenance. Eventually, the life cycle cost of the equipment will be reduced, as they are used until the end of their lives.
Until now, we have talked about the types of maintenance strategies and benefits and the history behind them. The following part will explain how Kavaken helps its customer perform predictive maintenance effectively.
