Robert bosch gmbh (20240131734). SYSTEM AND METHODS FOR MONITORING MACHINE HEALTH simplified abstract
Contents
- 1 SYSTEM AND METHODS FOR MONITORING MACHINE HEALTH
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEM AND METHODS FOR MONITORING MACHINE HEALTH - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SYSTEM AND METHODS FOR MONITORING MACHINE HEALTH
Organization Name
Inventor(s)
Sirajum Munir of PITTSBURGH PA (US)
Samarjit Das of WEXFORD PA (US)
SYSTEM AND METHODS FOR MONITORING MACHINE HEALTH - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240131734 titled 'SYSTEM AND METHODS FOR MONITORING MACHINE HEALTH
Simplified Explanation
The system described in the abstract is a sensor-based system that collects raw signals associated with a machine's environment, processes the signals, classifies them into different categories, and outputs a heat map overlaid on an image of the environment.
- Sensors installed near a machine collect raw signals related to the machine's environment.
- The system includes a processor that denoises the raw signals, extracts features, classifies them into normal, abnormal, or potential-abnormal classes, and creates fusion data with time-stamp information.
- The output of the system is a heat map displayed on an overlaid image of the machine's environment.
Potential Applications
This technology can be applied in various industries such as manufacturing, healthcare, and transportation for predictive maintenance, anomaly detection, and performance optimization.
Problems Solved
This technology helps in early detection of machine malfunctions, reduces downtime, and improves overall efficiency by analyzing and classifying raw signals from the machine's environment.
Benefits
The system provides real-time monitoring, predictive analytics, and visualization of machine data, leading to cost savings, increased productivity, and improved safety.
Potential Commercial Applications
The technology can be used in industrial IoT systems, smart buildings, and healthcare monitoring devices for condition monitoring, fault detection, and performance analysis.
Possible Prior Art
Similar sensor-based systems exist in the field of predictive maintenance and anomaly detection, but the specific method of denoising raw signals, extracting features, and creating fusion data with time-stamp information may be novel.
Unanswered Questions
How does the system handle data privacy and security concerns?
The article does not address the potential privacy and security implications of collecting and processing sensitive machine data. Implementing encryption, access controls, and data anonymization techniques could address these concerns.
What are the scalability limitations of the system?
The scalability of the system in terms of handling a large number of sensors, processing massive amounts of data, and maintaining real-time performance is not discussed. Addressing scalability issues could be crucial for widespread adoption in large-scale industrial applications.
Original Abstract Submitted
a system that includes one or more sensors installed in proximity to a machine configured to collect raw signals associated with an environment of the machine, are multi-layer spatial data that include time-stamp data. the system may include a processor in communication with the sensors and programmed to receive one or more raw signals, denoise the one or more raw signals to obtain a pre-processed signal, extract one or more features from the pre-processed signals, classify the one or more features to an associated class, wherein the associated class includes one or more of a normal class, abnormal class, or a potential-abnormal class, create fusion data by fusing the one or more features utilizing the associated class and the time-stamp data, and output a heat map on an overlaid image of the environment.
