PraxedoOur blog Benefits of Preventive Maintenance Systems
  • Field Service Management
  • Internet of Things
  • Gestion d’interventions
  • Artificial Intelligence
  • Technologies

Benefits of Preventive Maintenance Systems

Xavier Biseul
September 11, 2018
11 min. min.

A growing number of companies have realized that switching to predictive maintenance helps them reduce operating costs and extend equipment lifespan. As a result, there are compelling examples of the benefits in industries ranging from aeronautics and automobiles to railways and energy.
As the old saying goes, prevention is better than cure. And predictive maintenance is all about prevention. With the ability to track equipment operation over time and anticipate breakdowns before they occur, predictive maintenance is like a dream come true for service professionals.
Predictive maintenance replaces existing maintenance models that are increasingly showing their limitations. For example, with corrective maintenance, the failure has already occurred before anyone steps in to fix it. With preventive maintenance, the maintenance periods are predetermined according to statistics for wear rates, but parts may end up being replaced when they still have considerable life left in them.

The saying “prevention is better than cure” is very relevant to maintenance activities. In today’s era of big data, Industry 4.0 and the Internet of Things (IoT), new technologies are enabling field service companies to evolve from corrective maintenance strategies to predictive strategies.   A June 2017 study conducted by the Aberdeen Group Research Institute shows that 37% of service companies plan to use predictive analytics tools to enhance their maintenance strategies.   In this article, we will introduce you to predictive maintenance and tell you a bit about the benefits it can bring to your business.  

Corrective, preventive and predictive maintenance

Let’s start with a few definitions.   Corrective maintenance, which is also called curative maintenance, makes sure that equipment is repaired after a failure has occurred. Failures are not predicted or anticipated. Instead, technicians intervene to correct the problem after the breakdown.   Preventive maintenance makes sure that equipment receives regularly scheduled service to help keep it running smoothly. Because maintenance activities are scheduled based on the expected lifecycle for equipment, preventive maintenance can help to anticipate and avoid potential failures.   The timing of problem detection is the main drawback with preventive maintenance. If wear or a defect is detected on a part in fully functioning equipment, that part is replaced as a precaution in case it could lead to a failure down the road. This approach often results in unexpected expenses that customers could have avoided or postponed.   Predictive maintenance “helps determine the condition of in-service equipment in order to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance because tasks are performed only when warranted,” according to the Wikipedia definition.   Predictive maintenance pushes maintenance tasks a step further than preventive maintenance.   To predict when maintenance is required, intelligent sensors are added to equipment so they can transmit data about equipment operation. By analyzing the data sent by the sensors, field service companies can adopt a “just in time” approach, repairing or replacing equipment just before a failure occurs to optimize the amount of time equipment can be used.   Predictive maintenance forecasts technical issues instead of allowing them to occur. With a predictive maintenance strategy, field service companies can schedule service calls in advance to avoid unexpected stoppages in equipment operation.  

Setting up a predictive maintenance strategy

The first step in establishing a predictive maintenance strategy is to install sensors on the equipment. The sensors allow you to collect data about the equipment’s operational state. You can then use software to analyze the data according to specific parameters. This lets you establish what normal behavior looks like and set the thresholds at which imminent failure warnings are generated.  

Smart sensors

A number of different types of industrial sensors are available. Many types of modern equipment come with smart sensors already installed. Here are just a few of the many types of sensors used today:  

  • Ultrasonic flaw detection
  • Infrared thermographic analysis
  • Vibration analysis
  • Fluid analysis
  • Oil analysis
  • Spectral analysis


Artificial intelligence and predictions

Analytics tools help you reveal the insight that is hidden in the data collected by sensors so you know where maintenance is required. This is the world of artificial intelligence (AI). AI algorithms can find and analyze the data that is characteristic of an imminent breakdown. From there, the algorithms model similar data combinations and failure scenarios and set warning thresholds accordingly. When one of the combinations reappears, a failure is considered to be imminent.   When field service management software, such as the solution offered by Praxedo, is connected to the analytics software, you can automate decisions about which maintenance activities must be scheduled and in what timeframe.  

Integration of information systems

Integrating all of the different information systems involved is critical to the success of a predictive maintenance strategy. Maintenance data must be centralized so it can be accessed by the analytics software. Cloud technologies enable this data centralization. The Aberdeen Group study indicates that companies using predictive analytics technologies to enable maintenance strategies are 33% more efficient in creating a unified view of their data from all of their information systems.  

Predictive maintenance costs and benefits

Implementing a predictive maintenance strategy provides significant savings over corrective and preventive maintenance because tasks are only performed at the appropriate time. You can anticipate exactly when a failure will occur and intervene only when the need is clearly established. Timing, cost, and operational tradeoffs are balanced: costs are reduced because service is not provided before it is needed, and complete breakdowns are avoided because service is provided just in time.  

Initial investments are needed…

Implementing a predictive maintenance strategy can be a significant cost to your business. You’ll need the sensors necessary to ensure continuous equipment monitoring as well as analytics software to extract insight from the sensor data. You also need to consider the cost of training your teams to take full advantage of these new data analysis tools. Then, you need to plan the time to develop and deploy the program….  

To enable significant operational benefits

In return for the time and money you put into your predictive maintenance program, there are enormous benefits, especially over the long term:  

  • A significant reduction in equipment breakdowns and downtime
  • Better equipment monitoring so you can anticipate minor incidents that could lead to larger problems
  • Optimal use of equipment (until the moment before failure) and a longer life expectancy
  • Higher equipment reliability to optimize production
  • Better ability to plan maintenance activities and prepare maintenance teams for the required tasks
  • Better spare parts management
  • Lower maintenance and downtime costs for equipment

  As an example, in 2014 the consulting firm, Roland Berger, produced a study indicating that energy companies that implemented predictive maintenance programs successfully eliminated up to 75% of equipment failures.   This proof point perfectly captures the concrete advantages of setting up a predictive maintenance program, particularly in the industrial sector, where maintenance-related interruptions in production facilities can very quickly become very expensive.   So, when will you switch to predictive maintenance?    

IA + IoT = The ideal combination

Predictive maintenance combines artificial intelligence (AI) and the Internet of Things (IoT) to compare real-time data from sensors on connected equipment to the equipment’s history. These comparisons reveal the warning signs of potential breakdowns to proactively trigger maintenance activities.
Since 2010, several factors have come together to enable greater use of predictive maintenance. Recent advances in machine learning and deep learning have been key. And the decrease in the cost of adding sensors to large numbers of connected objects has opened the door to full-scale industrial IoT strategies.

Longer equipment lifespans, less downtime

Now that technologies have matured, companies at the forefront of predictive maintenance are seeing returns on their investments through longer equipment lifespans and less downtown.
At the same time, service companies can work more productively. For example, they can optimize management of spare parts inventories by replacing only those parts that must be replaced. On the human resources side, they can operate more efficiently, ensuring the best-suited technician is sent to the right customer site at the right time.

Aeronautics leads the way in predictive maintenance

Aeronautics was one of the first industries to adopt predictive maintenance. After fuel, maintenance is the biggest expense for these companies. With the exponential growth of air traffic, every airline is looking to minimize the amount of time aircraft spend sitting on the ground.
The maintenance, repair and overhaul (MRO) market has a bright future. According to the American global management consulting firm, Oliver Wyman, the MRO market is expected to grow from $75.6 billion in 2017 to $109 billion in 2027. It’s a lucrative market that applies to every player in the aeronautics chain from airlines and aircraft manufacturers to engine manufacturers.
Air France-KLM, an airline holding company based in Paris, sells its own predictive maintenance solution called Prognos. In 2017, the Airbus group launched the Skywise platform, which it uses to collect data from thousands of aircraft in flight. The data is then used by airlines, including British low-cost carrier, EasyJet, to improve operations. Safran Aircraft Engines has taken a different approach, adding sensors to its engines so it can continuously monitor power, temperature and pressure levels. Google, Microsoft, IBM and Amazon Web Services also offer predictive maintenance solutions.

Railways are optimizing and securing aging networks

Other types of transportation companies have different challenges. In the railway industry, aging infrastructure is a major concern. In July 2018, a major fire at a Paris-area electrical center operated by French railway company, SNCF, delayed tens of thousands of travelers, paralyzed traffic, cut power to thousands of surrounding homes and resulted in extensive evacuations.
Security is another issue for railways. SNCF has been running a very ambitious industrial IoT program for a few years now. In 2016, the company announced it would invest up to 500 million euros to improve the security of its network.

Drones inspect tracks

The improvements start by using sensors to monitor and prevent track buckling and other deformations along 30,000 km of rail lines. Railway switches, level crossings and land bordering tracks are also closely inspected. SNCF also uses drones to identify potential obstacles along the tracks, particularly surrounding vegetation that could interfere with trains.
On the infrastructure side, automated monitoring of pantographs — the articulating arms on the roof of electric rail vehicles that conduct the electrical current — help to prevent breaks in the messenger wire, also known as the catenary.
Rolling stock is gradually being connected to help avoid breakdowns in air conditioning, which can be very damaging during heat waves. In addition, sensors are being added to sliding doors and passenger seating to optimize replacement schedules. SNCF is also using sensors to optimize operation of elevators, escalators and lighting in stations.

Automation drives the automotive industry

Let’s move on from public transportation to cars. The automotive industry hasn’t reached the same level of maturity as other industries in terms of predictive maintenance strategies. Maintenance still follows the conventional schedule where an overhaul is recommended every 30,000 km or so and the timing belt should be replaced after five years or 120,000 km.
However, the proliferation of connected cars gives manufacturers the opportunity to capitalize on the mountain of data they’re collecting to offer remote diagnostic services. Managers of vehicle fleets are particularly interested in this level of data management to optimize vehicle operations.
As a wider range of players get involved in the automotive industry, the focus on data will become increasingly important. Apple and Google are already involved with their CarPlay and Android Auto embedded operating systems. And startups, such as Drust and Carfit, are developing technology that can be connected to the car’s on-board diagnostic socket or used to analyze vehicle vibrations to anticipate potential failures.

Energy companies benefit from real-time data

With the advent of smart meters, companies that manage energy distribution networks for electricity, gas and water have new opportunities to collect data that can be used to predict maintenance requirements and reduce the number of times workers need physical access to infrastructure.
French utility company, Suez, offers software called Aquadvanced that controls drinking water networks in real time, pointing out that “90% of leaks are invisible”. Daher, which manufactures high-performance valves for the nuclear energy industry, is using sensors to analyze vibration, temperature and flow levels in its facilities so it can identify degraded performance levels and predict possible failures.
In the renewable energy sector, Engie has invested about $14 million U.S. in its Darwin digital platform. The idea behind Darwin is to analyze the data transmitted by sensors installed on equipment in Engie’s wind and solar farms. Factors such as the speed that wind turbine blades rotate and the temperature of the photovoltaic panels will be monitored 24/7. According to French financial media, the move will allow Engie to save about $28 million U.S. in operating and maintenance costs in just five years. That’s a pretty fast return on investment.


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