Using AI-driven predictive maintenance to prevent hydraulic system failures

Modern-day hydraulics are an essential part of an industry’s function. Massive machines like construction excavators and mining drills use hydraulics to deliver force and precision while operating. If a hydraulic system fails, it can cost companies more through extended downtime, a hazardous working environment, loss of production, and repair costs.

Reactive and preventive maintenance strategies are no longer a viable solution for today’s high-demand industrial settings. AI-Powered Predictive Maintenance is a solution for providing a level of reliability to hydraulic systems through predictive maintenance instead of reactive.

The combination of Artificial Intelligence (AI) with Internet of Things (IoT) sensors, data analytics, and machine learning is revolutionizing the way hydraulic systems are maintained through moving from a reactive to predictive maintenance cycle and eliminating failures before they happen.

Causes of hydraulic system failures

Hydraulic systems fail due to many reasons, including: 

Contaminated hydraulic fluid, Seal degradation, Pump wear and cavitation, Overheating, Pressure spikes, Valve failure, Hose fatigue, and leaking.

For instance, pump cavitation with Bosch Rexroth and Parker Hannifin pumps can create noise, vibration, and, if continuous, a catastrophic failure. Additionally, inadequate fluid management in mobile equipment can significantly decrease the life of hydraulic systems.

Traditional inspection techniques are usually late in identifying problems; a technician sees abnormal noises, pressure drops, or overheating, but by this point, there could have been significant internal damage.

By using predictive maintenance that is driven by AI, we can detect hydraulic system liquid and equipment-related problems earlier than normal.

The term 'artificial intelligence (AI) driven predictive maintenance' refers to the maintenance of machinery using AI. The tools used in this process include:

• Smart sensors
• Real-time data acquisition
• Machine learning algorithms
• Cloud computing or edge computing
• Historical performance data

The outcome of predictive maintenance is to continually monitor all aspects of the machinery being maintained and identify patterns that suggest the machinery is becoming worn out, or that it is showing unexpected behaviour, resulting in predicting future mechanical failures.

Unlike traditional time-based maintenance schedules, AI systems determine how the machinery is currently functioning, based on the following parameters:

• Pressure changes
• Temperature changes
• Flow rate anomalies
• Vibrational characteristics
• Oil contamination

This allows maintenance crews to perform maintenance on a just-in-time basis, thereby increasing operating efficiency while decreasing costs.

How AI is used in hydraulic systems

1. Data collection through sensors

Modern hydraulic equipment is equipped with smart sensors that measure the following parameters:

• Pressure
• Flow
• Temperature
• Vibration
• Fluid quality

The sensor data is transmitted to a computer, which is responsible for tracking and monitoring the equipment in real-time. In the case of Caterpillar Inc.'s construction equipment, onboard telematics computers collect large volumes of performance data.

2. Data processing and machine learning

To analyze data and apply algorithms, AI algorithms can:

• Identify patterns of normal operation
• Detect anomalous patterns
• Identify potential early signs of failure
• Predict the likelihood of failure

For example, if the pump's vibration increases at a gradual rate and the oil temperature also rises gradually, the potential for bearing failure exists. With a machine learning model trained on prior failure data, these cycles of potential failure can be flagged as much as several weeks in advance, alerting maintenance to take action.

3. Predictive alerts & maintenance scheduling

Based on the detection of an "abnormal" pattern, the system will produce:

• Maintenance alerts
• Risk scores
• Estimates of Remaining Useful Life (RUL)
• Recommended actions

Based on the maintenance alert, the maintenance manager can arrange for service to be performed during a planned downtime period, rather than having to react to an unexpected failure.

Advantages of using AI-driven predictive maintenance

1) Minimizing unplanned downtime.

Unplanned downtime is one of the largest costs associated with operational activities in the mining, construction, and manufacturing sectors. Monitoring via AI can greatly reduce the number of hydraulic equipment malfunctions due to unexpected failures.

Because the potential for pump or valve failures can be identified by using AI-based programs, parts can be replaced during scheduled service.

2) Maximizing equipment life cycle

Hydraulic components are expensive. Identification of issues such as contamination or pressure imbalances can minimize subsequent failures through early identification.

For example:

Identifying a failing seal early can prevent contamination from occurring to the fluid.

Monitoring the oil condition can help to avoid scoring or excessive wear to the internal surfaces.

Monitoring for overheating will provide protection to your hydraulic system as a whole.

Using AI programs will provide you with the most accurate information regarding how efficiently your components are functioning at any point in time and, therefore, will allow you to maximize the service life of your hydraulic components.

3) Reducing maintenance expenses

Rather than issuing replacement components prior to their reaching end-of-life, traditional preventive maintenance is less reliable than predictive maintenance.

Instead of relying upon fixed intervals when changing hydraulic oil, predictive maintenance will evaluate the condition of hydraulic oil in real time, allowing for changing fluid only when the chemical composition becomes critically degraded.

4) Increased occupational safety

Preventative maintenance that fails to provide advance notice of mechanical failures will create unsafe conditions through:

• Sudden Drop of Loads
• Burst Hoses
• Fire Threat Resulting from Fluid Leaks
• Instability of Equipment

These factors may result in occupational injuries; predictive maintenance would allow for preventative repairs on hydraulic components and thereby eliminate potential hazards from unanticipated mechanical breakdown.

5) Improved inventory processes

By utilizing predictive maintenance methods, organizations will be able to:

• Optimize Inventory of Spare Parts
• Avoiding the Cost Associated with Emergency Shipping
• To Optimize Utilize Parts Other Than Warehouse Overstocking

AI-based Inventory Will Ensure That Organizations Have Appropriate Parts Available at Appropriate Times.

Case study examples

Construction equipment

There are several pieces of construction equipment that utilize hydraulic systems (for example, excavators, loaders, and cranes). The utilization of AI-based monitoring will identify:

• Pump Efficiency Problems
• Wear on Cylinder Seals
• Pressure Irregularities
• Fatigue In Hydraulic Hoses

Manufacturing and injection molding

The operation of hydraulic presses and molding machines is extremely dependent on accurate control of hydraulic fluid pressures. Small changes in hydraulic fluid pressures can adversely affect the quality of produced goods.

AI-based monitoring of hydraulic systems allows organizations the opportunity to identify changes in hydraulic fluid pressure by measuring:

• Pressure Consistency
• Valve Response Time
• Thermal Fluctuations

Consequently, the use of AI-based monitoring in manufacturing and injection molding will both significantly reduce scrap rates and improve overall production efficiency.

Agromially

Many of today’s tractors, harvesters, and heavy-duty materials handling machinery are built with sophisticated hydraulic circuitry that allows their various implements/attachments to perform at optimum efficiency. If you are an owner/operator of utility tractor types such as the Bobcat UT6066 or similar model tractors, the ability to perform predictive maintenance inspections will provide you with great benefits over time, especially in agricultural environments where the presence of dust contamination and long duration of operation create greater potential for hydraulic system failures.

Monitoring your hydraulic systems’ performance will result in significantly less downtime through critical harvest periods of the year.

Mining and heavy industry

In mining operations, the hydraulic systems in use can be subject to many failures (e.g., failure of high-pressure pumps) and, thus, halt production altogether. Utilizing predictive AI provides the ability to monitor:

• High-pressure pumps
• Large hydraulic cylinders
• Rock breakers
• Drilling rigs

Early identification of potential failures will minimize costly interruptions to your operation.

What technologies enabling predictive maintenance using AI exist today?

Industrial internet of things (IIoT)

IIoT platforms connect hydraulic sensor data to centralized monitoring systems, allowing for remote diagnostics.

Edge computing

Edge computing devices are capable of processing data locally to the equipment (i.e., where the equipment is located), enabling:

1) Faster detection of anomalies
2) Lower dependency on the cloud
3) Less latency on critical systems

Digital twins

Digital twin is used to describe a virtual representation (usually electronic) of a particular hydraulic system or subassembly that simulates how it behaves in real-time operation (with a high degree of accuracy). The AI process continually compares live data against the model of the digital twin to identify occurrences of deviation.

Cloud analytics

Cloud-based analytics offer the ability to store vast quantities of historical data to improve the overall accuracy of the machine learning predictive model over time.

Implementing AI predictive maintenance presents challenges

Installing AI predictive maintenance technologies presents some significant challenges despite the many upsides.

1. High-quality data

AI functions based on data; if the data is inaccurate due to non-calibrated sensors or missing information, the accuracy of AI prediction will not be valid.

2. Investment to get started

There are a number of capital expenditures associated with getting your organization up and running with AI predictive maintenance technology, including the installation of sensors, upgrading the control system, and integrating data/information with the AI platform.

ROIs of AI predictive maintenance are typically achieved through reduced downtime and extended life of the equipment.

3. Older systems must be integrated into the new process

Older hydraulic systems may not have the digital technologies available. Retrofitting older systems with sensors and data/equipment may be extremely difficult.

4. Interpretation of AI-generated data is critical

Organizations will need to train their staff to interpret the data that is produced by the AI.

The future of hydraulic predictive maintenance

Hydraulic system predictive maintenance through artificial intelligence will eventually become intelligent, automatic, and data-driven.

Current trends include:

• Ability for hydraulic systems to self-adjust.
• Adaptive control through artificial intelligence.
• Autonomous condition monitoring.
• Use of blockchain in maintenance records.
• Fluid characteristics analytics. 

The future of hydraulic system predictive maintenance is to enable hydraulic systems to become self-diagnosing and to partially self-heal by changing operating conditions to prevent failure.

Best practices for implementing a predictive maintenance strategy using AI technology

If your business is looking into using AI technology to implement a Predictive Maintenance strategy, there are some steps you should take:

1. Start with the most critical equipment
2. Install high-quality sensors and actuators
3. Collect baseline performance data
4. Utilize a scalable AI platform
5. Train maintenance personnel on how to 
6. Measure ROI consistently
7. Continue to refine AI models

Begin with pilot projects in order to demonstrate cost savings and improvements in operations before rolling out a network-wide initiative across all locations.

AI-based Predictive Maintenance is revolutionizing the reliability of hydraulic systems. By continuously tracking the pressure, temperature, vibration, and condition of hydraulic fluids, AI will identify early warning signs of mechanical failure well in advance of a catastrophic breakdown.

The results of this new technology are numerous and include:

• Decreased Downtime
• Decreased Total Cost of Ownership
• Increased Lifecycle of Equipment
• Greater Safety of Employees
• Better Resource Planning

As industries continue to move towards automation/smart manufacturing, Predictive Maintenance will be a requirement.

For industries heavily reliant on hydraulic-powered equipment, such as Construction, Agriculture, Mining, and Manufacturing, investing in AI-enabled monitoring systems is a strategic investment that enhances efficiency and provides a competitive advantage.

The transition away from reactive Maintenance Practices towards predictive is not just a technological advancement but rather a change in the way we operate and maintain vital hydraulic infrastructure in today’s industrial world.