Predictive maintenance to optimize operating time

Predictive maintenance optimizes service intervals by determining the individual operating lifetimes. This maximizes the useful lifetime of systems, as maintenance costs are planned in sufficient time to meet predicted demand.

Predictive Maintenance

Optimize maintenance planning with Machine Learning

A technical installation is then reliably available if it is repaired in a timely manner, i.e. maintenance and repair are done directly. This will only succeed if the company can access appropriate resources. The appropriate skilled workers, spare parts and logistics are present.

If systems are operated without repair due to an unidentified need for maintenance of a component, other components are affected. The subsequent maintenance takes longer and becomes more expensive.

Fast reactions at the right time require reliable detection and prediction of damage events in order to provide the necessary resources as required. The goal is a maintenance strategy that detects faults before they occur and allows optimal maintenance to be planned.

© ITWM

Prognosis of the remaining useful life time of a plant by a COX regression

What is Predictive Maintenance?

A reliable prediction of future events is an integral part of any predictive maintenance system. An important key lies in the patterns of past events. The recognition of complex event patterns as well as their own dynamics and trends are the playing field of the system we developed.

According to DIN 3105, predictive maintenance is an extension of condition-based maintenance that predicts when the system will failure . The main goal here is to make the best possible use of the component's stock and, at the same time, to rectify any faults or failures before they occur.

© ITWM

Classification of failure events in an information space optimized by PCA

Five reasons for predictive maintenance

Predictive maintenance optimizes service intervals by determining the individual operating lifetimes. This maximizes the useful lifetime of systems, as maintenance costs are planned in sufficient time to meet predicted demand.

  • Early detection of anomalies in the plant or its performance.
  • Identification of root causes of unplanned failures / faults
  • Planable estimation of the remaining useful life of plants and components.
  • Reliable prediction of failure in the near future.
  • Efficient planning of upcoming maintenance actions.

Models and techniques

Depending on the objective, statistical or stochastic models recognize, classify, predict and even control the different states of your plant. Below are a few examples:

FIELD DesCriptivE DiagnostiCS PrEdiCtivE PrEsCriptivE
QUESTION WHAT is the operating state? WHY did the error occur? WHEN does the next error occur? HOW will the operation be continued?
Information

Data acquisition and pre-processing

Feature extraction

use cases Detect and assess
  • operating conditions
  • anomalies
  • events
Classify
  • anomalies
  • events
Predict
  • trends
  • life-time
  • events
Control
  • maintenance time
  • plant operation
Selection of our algorithms Detect and assess
  • signal analyses
  • threshold analyses
  • self-organizing maps
Classify
  • deep learning
  • decision trees
  • Bayesian networks
Predict
  • mixed-effect models
  • event models
  • joint modeling
Control
  • predictive control
  • reinforcement learning

 

 

Our portfolio and range of services

Predictive maintenance can be introduced quickly, especially where a wide range of data is already being collected. However, it will also be worthwhile retrofitting your plants with monitoring systems.

  Our Offer
Machinery and equipment

Sensor selection and placement

Virtual sensors

Condition monitoring

System modeling and simulation with digital twins

CommuniCation Integration into common IOT platforms, communication protocols and networks
DatA MANAGEMENT

Efficient data collection through design-of-experiments

System and event models

Data cubes

Forecasting and evaluation

Identification, implementation and application of suitable machine learning algorithms

Diagnosis and prognosis of data streams

Analytics, reports and dashboards

ProCess integration

Reliable control of connected systems

Optimization of maintenance and service

Enterprise resource and lifecycle planning