Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework
2. Review of Key Concepts and Trends
2.1. Overview of Maintenance Strategies
- Unplanned or reactive maintenance—typically allows for machinery to breakdown, after which it is analyzed and repaired.
- Planned or preventive maintenance—an assessment of the system is conducted at regular time intervals to determine whether any repair/replacement is necessary. It is important to note that the health of the system is not taken into consideration in establishing the time intervals.
- Predictive maintenance—a data-driven approach in which parameters concerning the health of the system are used to monitor the condition of the equipment and in determining the RUL.
2.2. Multi-Faceted Approach to PHM
- Data acquisition and preprocessing: For any predictive problem in maintenance to be solved, the availability of data is of utmost importance. IoT devices and smart sensors are typically used to acquire data in manufacturing settings. The data are recorded and evaluated in real-time as certain anomalies may be detected at an early stage by maintenance engineers or control systems. The collection of such data is extremely important as it provides vital information that helps to understand the relationships between the heterogenous components of the system. Once the data are collected, they are analyzed and preprocessed to ensure that crucial information which helps in failure detection is obtained.
- Degradation detection: Identifying that a component is degrading or that it is bound to fail is the next step once the data have been collected and prepared. Anomalies and failures can be detected using sensor readings and by other specified criteria, such as surface roughness, temperature, size of tools/equipment, etc.
- Diagnostics: Once a determination is made that a failure is occurring, understanding the cause of the failure is the next step. Failure types can be categorized to evaluate the extent of the failure, helping in finding its root causes. Operating conditions of individual components can be analyzed along with their interactions to help diagnose the cause of failures.
- Prognostics: With the ability to detect failures using diagnosing mechanisms, predictive methods are used to predict the system health to avoid potential failures. Model-based prognostics involve Physics-of-Failure (PoF) methods to assess wear and predict failure. However, such approaches are limited as even minor changes to the operations can result in poor predictive power. Data-driven approaches are becoming more common for prognostics with the use of DL and ML techniques. By using data-driven methods along with crucial information from physics-based methods, highly accurate predictions can be made about systems.
- Maintenance decisions: Based on results from the predictive methods developed, manufacturing enterprises can determine policies to be followed for maintenance planning that will help with less downtime, higher yield and a reduction in losses.
2.3. Challenges in Implementing PHM in the Industry
2.3.1. In Prognostics
- Insufficient failure data or excessive failure data may skew prediction of RUL
- Inadequate standards to assess prognostic models
- Lack of precise real-time assessment of RUL
- Uncertainty in determining accuracy and performance of prognostic models.
2.3.2. In Diagnostics
- Expertise required in diagnosis of failures
- Limitations due to lack of training and formal guidelines in authentication of diagnostic methods
- Difficulty in diagnosis due to outliers, noise in signal data and operating environment.
2.3.3. In Manufacturing
- Ability to effectively assess electronic components
- Integration of sensors and field devices with PHM standards
- Inconsistencies in data, data formats, and interoperability of data in manufacturing facilities
- Inadequate correspondence between production planning and control units and maintenance departments
- High level of complexity and heterogeneity in manufacturing systems.
2.3.4. In Enterprises
- Proactive involvement required towards maintenance to view PHM as a cost-saving approach and not a cost-inducing one
- Enterprises with legacy machines and equipment tend to go with one of the traditional approaches to maintenance, even though PHM methods are more effective
- Securing funding for PHM projects.
2.3.5. In Human Factors
- User friendly interfaces and applications
- Collection of expert knowledge
- Improvement in outlook towards implementing changes to existing mechanisms.
2.4. Overview of Prognostics Modeling Approaches
- Lack of readily available data in a standardized format
- Insufficient failure data due to imbalance in data classes
- Lack of physics-based parameters in the data.
2.5. Current Trends in PHM Research
2.5.1. Applications of Machine Learning in PHM
2.5.2. Applications of Deep Learning in PHM
2.5.3. Health Index Construction
2.5.4. PHM Using Manufacturing Paradigms
3. Research Gap and Proposal
4. An Interoperable Framework for SPHM in SM
4.1. Phase 1: Setup and Data Acquisition Phase
4.1.1. Shopfloor Setup
4.1.2. Data Collection and Understanding
4.2. Phase 2: Data Preparation and Analysis
4.2.1. Data Cleaning and Preprocessing
- Min–max normalization
- Mean normalization
- Unit Scaling
4.2.2. Signal Preprocessing
4.2.3. Feature Extraction
Time-Domain Feature Extraction
Frequency-Domain Feature Extraction
- = input signal at time ,
- = nT = n-th sampling instant, for n 0,
- = spectrum of x at frequency ,
- = sample from k-th frequency in radians per second,
- T = sampling interval in seconds,
- = 1/T = sampling rate or samples per second,
- = total number of samples in signal.
- = signal amplitude at sample,
- = DTFT of x at sample.
Time-Frequency Domain Features
4.2.4. Feature Evaluation and Selection
4.3. Phase 3: SPHM Modeling and Evaluation
5. Case Study: Milling Machine Operation
5.1. Phase 1: Milling Machine Setup and Data Acquisition
5.1.1. Milling Machine and Sensor Setup
5.1.2. Data Collection and Understanding
5.2. Phase 2: Data Preparation and Analysis
5.2.1. Data Cleaning and Preprocessing
5.2.2. Signal Preprocessing
5.2.3. Feature Extraction
5.2.4. Feature Evaluation and Selection
7. Conclusions and Future Work
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
|1||1||0||2||1.5||0.5||1||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim|
|1||2||NaN||4||1.5||0.5||1||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim|
|1||3||NaN||6||1.5||0.5||1||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim|
|1||4||0.11||7||1.5||0.5||1||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim|
|1||5||NaN||11||1.5||0.5||1||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim||9000 × 1 dim|
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|3||Root Mean Square|
|1||Maximum Band Power Spectrum|
|2||Sum of Band Power Spectrum|
|3||Mean of Band Power Spectrum|
|4||Variance of Band Power Spectrum|
|5||Skewness of Band Power Spectrum|
|6||Kurtosis of Band Power Spectrum|
|7||Relative Spectral Peak per Band|
|Feature Name||Feature Description|
|case||Cases from number 1 to 16|
|run||Counting the runs in each case|
|VB||Flank wear observed in the cutting tool, not observed after each run|
|time||Time taken for each experiment, resets after completion of each case|
|DOC||Depth of Cut, kept constant in each case|
|feed||Feed, kept constant in each case|
|material||Material, kept constant in each case|
|smcAC||AC current at spindle motor|
|smcDC||DC current at spindle motor|
|vib_table||Vibration measured at table|
|vib_spindle||Vibration measured at spindle|
|AE_table||Acoustic emission measured at table|
|AE_spindle||Acoustic emission measured at spindle|
|SPHM Phase||Steps||Relevant Section||Implementation on Use-Case|
|Phase 1: Setup and Data Acquisition|
|Phase 2: Data Preparation and Analysis|
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Sundaram, S.; Zeid, A. Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework. Sensors 2021, 21, 5994. https://doi.org/10.3390/s21185994
Sundaram S, Zeid A. Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework. Sensors. 2021; 21(18):5994. https://doi.org/10.3390/s21185994Chicago/Turabian Style
Sundaram, Sarvesh, and Abe Zeid. 2021. "Smart Prognostics and Health Management (SPHM) in Smart Manufacturing: An Interoperable Framework" Sensors 21, no. 18: 5994. https://doi.org/10.3390/s21185994